Combination Of Satellite Observations Research Articles

Satellite monitoring of ice features to ensure safety of offshore operations in the Arctic seas

Methods for processing satellite data on ice features that pose a threat to the safety of offshore operations in the Arctic seas are considered. They are divided into interactive, automated, and automatic methods. The methods are illustrated by examples of archival satellite data on the Russian Arctic sector. Radar and optical data have been used as the satellite information source. It is shown that the successful satellite monitoring of dangerous ice features requires the optimal combination of satellite observations at various stages that provide for a synergistic approach to the data assimilation of different spectral bands obtained using different spacecraft.

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.

Combining satellite observations and reanalysis energy transports to estimate global net surface energy fluxes 1985–2012

AbstractTwo methods are developed to estimate net surface energy fluxes based upon satellite‐derived reconstructions of radiative fluxes at the top of atmosphere and the atmospheric energy tendencies and transports from the ERA‐Interim reanalysis. Method 1 applies the mass‐adjusted energy divergence from ERA‐Interim, while method 2 estimates energy divergence based upon the net energy difference at the top of atmosphere and the surface from ERA‐Interim. To optimize the surface flux and its variability over ocean, the divergences over land are constrained to match the monthly area mean surface net energy flux variability derived from a simple relationship between the surface net energy flux and the surface temperature change. The energy divergences over the oceans are then adjusted to remove an unphysical residual global mean atmospheric energy divergence. The estimated net surface energy fluxes are compared with other data sets from reanalysis and atmospheric model simulations. The spatial correlation coefficients of multiannual means between the estimations made here and other data sets are all around 0.9. There are good agreements in area mean anomaly variability over the global ocean, but discrepancies in the trend over the eastern Pacific are apparent.

Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System

Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10‐day forecasts on a 1/4°‐resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sea‐level anomaly and sea‐surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D‐Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis‐based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium‐range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open‐water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.

Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction

The vegetation state can have a prominent influence on the global energy, water and carbon cycles. This has been particularly evident during extreme conditions in recent years (e.g. Europe 2003 and Russia 2010 heat waves, Horn of Africa 2010 drought, and Australia 2010 drought recovery). Weather parameters are sensitive to the vegetation state and particularly to albedo and Leaf Area Index (LAI) that controls the partitioning of the surface energy fluxes into latent and sensible fluxes, and the development of planetary boundary conditions and clouds.An optimal interpolation analysis of a satellite-based surface albedo and LAI is performed through the combination of satellite observations and derived climatologies, depending on their associated errors. The final analysis products have smoother temporal evolution than the direct observations, which makes them more appropriate for environmental and numerical weather prediction.The impact of assimilating these near-real-time (NRT) products within the land surface scheme of the European Centre of Medium-Range Weather Forecasts (ECMWF) is evaluated for anomalous years. It is shown that: (i) the assimilation of these products enables detecting/monitoring extreme climate conditions where the LAI anomaly could reach more than 50% and in wet years albedo anomaly could reach 10% , (ii) extreme NRT LAI anomalies have a strong impact on the surface fluxes, while for the albedo, which has a smaller inter-annual variability, the impact on surface fluxes is small, (iii) neutral to slightly better agreement with in-situ surface soil moisture observations and surface energy and CO2 fluxes from eddy-covariance towers is obtained, and (iv) in forecast using a land–atmosphere coupled system, the assimilation of NRT LAI reduces the near-surface air temperature and humidity errors both in wet and dry cases, while NRT albedo has a small impact, mainly in wet cases (when albedo anomalies are more noticeable).

Global Lightning Parameterization from CMIP5 Climate Model Output

AbstractLightning is best known as a component of severe weather, but diverse communities of researchers, such as those in atmospheric chemistry and global fire modeling, have long recognized that lightning plays a significant role in many components of the Earth system. Global climate models are unlikely to be able to simulate lightning from first principles, but the same models do simulate many parameters related to convection, including total precipitation rate, convective precipitation rate, and convective mass flux. This study combines satellite observations of lightning and CMIP5 climate model simulations to derive an empirical parameterization of monthly lightning in terms of monthly simulated convective parameters. Convective mass flux best captures the spatiotemporal distribution of observed lightning. Derived lightning seasonality is captured with 95% confidence over 69% of land but only 30% of ocean. Spatially, the correlation of derived lightning and observed lightning is 0.74. Overall, global observations suggest lightning occurs at an annual rate of 47 flashes per second, while lightning from the parameterization occurs at 44 flashes per second. A robust feature of the relationship between lightning and climate model convective parameters is that lightning flash rate increases linearly with increases in convective precipitation rate and in convective mass flux for a significant subset of the total range of those convective parameters. Namely, this linear proportionality is evident when the convective precipitation rate is less than 4–5 mm day−1 and the convective mass flux is less than 15–16 kg m−2 h−1, which account for about 90% of the values simulated by the climate models.

Modeling grassland spring onset across the Western United States using climate variables and MODIS-derived phenology metrics

Vegetation phenology strongly controls photosynthetic activity and ecosystem function and is essential for monitoring the response of vegetation to climate change and variability. Terrestrial ecosystem models require robust phenology models to understand and simulate the relationship between ecosystems and a changing climate. While current phenology models are able to capture inter-annual variation in the timing of vegetation spring onset, their spatiotemporal performances are not well understood. Using green-up dates derived from MODIS, we test 9 phenological models that predict the timing of grassland spring onset via commonly available climatological variables. Model evaluation using satellite observations suggests that Modified Growing-Degree Day (MGDD) models and Accumulated Growing Season Index (AGSI) models achieve reasonable accuracy (RMSE<20days) after model calibration. Inclusion of a photoperiod trigger and varied critical forcing thresholds in the temperature-based phenology model improves model applicability at a regional scale. In addition, we observe that AGSI models outperform MGDD models by capturing inter-annual phenology variation in large semi-arid areas, likely due to the explicit consideration of water availability. Further validation based on flux tower sites shows good agreement between the modeled timing of spring onset and references derived from satellite observations and in-situ measurements. Our results confirm recent studies and indicate that there is a need to calibrate current phenology models to predict grassland spring onsets accurately across space and time. We demonstrate the feasibility of combining satellite observations and climatic datasets to develop and refine phenology models for characterizing the spatiotemporal patterns of grassland green-up variations.

Probabilistic 0–1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data

AbstractThe Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

Physical analysis on improving the recovery accuracy of the Earth's gravity field by a combination of satellite observations in along-track and cross-track directions

The physical investigations on the accuracy improvement to the measurement of the Earth's gravity field recovery are carried out based on the next-generation Pendulum-A/B out-of-plane twin-satellite formation in this paper. Firstly, the Earth's gravity field complete up to degree and order 100 is, respectively, recovered by the collinear and pendulum satellite formations using the orbital parameters of the satellite and the matching accuracies of key payloads from the twin GRACE satellites. The research results show that the accuracy of the Earth's gravity field model from the Pendulum-A/B satellite formation is about two times higher than from the collinear satellite formation, and the further improvement of the determination accuracy of the Earth's gravity field model is feasible by the next-generation Pendulum-A/B out-of-plane twin-satellite formation. Secondly, the Earth's gravity field from Pendulum-A/B complete up to degree and order 100 is accurately recovered based on the orbital parameters of the satellite (e.g., an orbital altitude of 400 km, an intersatellite range of 100 km, an orbital inclination of 89° and an orbital eccentricity of 0.001), the matching accuracies of space-borne instruments (e.g. 10−6 m in the intersatellite range, 10−3 m in the orbital position, 10−6 m/s in orbital velocity, and 10−11 m/s2 in non-conservative force), an observation time of 30 days and a sampling interval of 10 s. The measurement accuracy of the Earth's gravity field from the next-generation Pendulum-A/B out-of-plane twin-satellite formation is full of promise for being improved by about 10 times compared with that from the current GRACE satellite formation. Finally, the physical requirements for the next-generation Pendulum-A/B out-of-plane twin-satellite formation are analyzed, and it is proposed that the satellite orbital altitude be preferably designed to be close to 400±50 km and the matching precision of key sensors from the Pendulum-A/B mission be about one order of magnitude higher than from the GRACE program.

Improving volcanic sulfur dioxide cloud dispersal forecasts by progressive assimilation of satellite observations

Forecasting the dispersal of volcanic clouds during an eruption is of primary importance, especially for ensuring aviation safety. As volcanic emissions are characterized by rapid variations of emission rate and height, the (generally) high level of uncertainty in the emission parameters represents a critical issue that limits the robustness of volcanic cloud dispersal forecasts. An inverse modeling scheme, combining satellite observations of the volcanic cloud with a regional chemistry-transport model, allows reconstructing this source term at high temporal resolution. We demonstrate here how a progressive assimilation of freshly acquired satellite observations, via such an inverse modeling procedure, allows for delivering robust sulfur dioxide (SO2) cloud dispersal forecasts during the eruption. This approach provides a computationally cheap estimate of the expected location and mass loading of volcanic clouds, including the identification of SO2-rich parts.

Global assessment of ocean carbon export by combining satellite observations and food‐web models

AbstractThe export of organic carbon from the surface ocean by sinking particles is an important, yet highly uncertain, component of the global carbon cycle. Here we introduce a mechanistic assessment of the global ocean carbon export using satellite observations, including determinations of net primary production and the slope of the particle size spectrum, to drive a food‐web model that estimates the production of sinking zooplankton feces and algal aggregates comprising the sinking particle flux at the base of the euphotic zone. The synthesis of observations and models reveals fundamentally different and ecologically consistent regional‐scale patterns in export and export efficiency not found in previous global carbon export assessments. The model reproduces regional‐scale particle export field observations and predicts a climatological mean global carbon export from the euphotic zone of ~6 Pg C yr−1. Global export estimates show small variation (typically &lt; 10%) to factor of 2 changes in model parameter values. The model is also robust to the choices of the satellite data products used and enables interannual changes to be quantified. The present synthesis of observations and models provides a path for quantifying the ocean's biological pump.

Comparison of modeled and observed effects of radiation belt electron precipitation on mesospheric hydroxyl and ozone

Observations have shown that mesospheric hydroxyl (OH) is affected by energetic electron precipitation (EEP) at magnetic latitudes connected to the outer radiation belt. It is not clear, however, if the current satellite‐based electron flux observations can be used to accurately describe EEP in atmospheric models. We use the Sodankylä Ion and Neutral Chemistry (SIC) model to reproduce the changes in OH and ozone observed by the Microwave Limb Sounder (MLS/Aura) during four strong EEP events. The daily mean electron energy‐flux spectrum, needed for ionization rate calculations, is determined by combining the Medium Energy Proton and Electron Detector fluxes and spectral form from the instrument for the detection of particles high‐energy electron detector on board the DEMETER satellite. We show that in general SIC is able to reproduce the observed day‐to‐day variability of OH and ozone. In the lower mesosphere, the model tends to underestimate the OH concentration, possibly because of uncertainties in the electron spectra for energies &gt;300 keV. The model predicts OH increases at 60–80 km, reaching several hundred percent at 70–80 km during peak EEP forcing. Increases in OH are followed by ozone depletion, up to several tens of percent. The magnitude of modeled changes is similar to those observed by MLS and comparable to effects of individual solar proton events. Our results suggest that the combined satellite observations of electrons can be used to model the EEP effects above 70 km during geomagnetic storms, without a need for significant adjustments. However, for EEP energies &gt;300 keV impacting altitudes &lt;70 km, correction factors may be required.

Inverting for volcanic SO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; flux at high temporal resolution using spaceborne plume imagery and chemistry-transport modelling: the 2010 Eyjafjallajökull eruption case study

Abstract. Depending on the magnitude of their eruptions, volcanoes impact the atmosphere at various temporal and spatial scales. The volcanic source remains a major unknown to rigorously assess these impacts. At the scale of an eruption, the limited knowledge of source parameters, including time variations of erupted mass flux and emission profile, currently represents the greatest issue that limits the reliability of volcanic cloud forecasts. Today, a growing number of satellite and remote sensing observations of distant plumes are becoming available, bringing indirect information on these source terms. Here, we develop an inverse modelling approach combining satellite observations of the volcanic plume with an Eulerian regional chemistry-transport model (CHIMERE) to characterise the volcanic SO2 emissions during an eruptive crisis. The May 2010 eruption of Eyjafjallajökull is a perfect case study to apply this method as the volcano emitted substantial amounts of SO2 during more than a month. We take advantage of the SO2 column amounts provided by a vast set of IASI (Infrared Atmospheric Sounding Interferometer) satellite images to reconstruct retrospectively the time series of the mid-tropospheric SO2 flux emitted by the volcano with a temporal resolution of ~2 h, spanning the period from 1 to 12 May 2010. We show that no a priori knowledge on the SO2 flux is required for this reconstruction. The initialisation of chemistry-transport modelling with this reconstructed source allows for reliable simulation of the evolution of the long-lived tropospheric SO2 cloud over thousands of kilometres. Heterogeneities within the plume, which mainly result from the temporal variability of the emissions, are correctly tracked over a timescale of a week. The robustness of our approach is also demonstrated by the broad similarities between the SO2 flux history determined by this study and the ash discharge behaviour estimated by other means during the phases of high explosive activity at Eyjafjallajökull in May 2010. Finally, we show how a sequential IASI data assimilation allows for a substantial improvement in the forecasts of the location and concentration of the plume compared to an approach assuming constant flux at the source. As the SO2 flux is an important indicator of the volcanic activity, this approach is also of interest to monitor poorly instrumented volcanoes from space.

Diagnosing Warm Frontal Cloud Formation in a GCM: A Novel Approach Using Conditional Subsetting

Abstract This study analyzes characteristics of clouds and vertical motion across extratropical cyclone warm fronts in the NASA Goddard Institute for Space Studies general circulation model. The validity of the modeled clouds is assessed using a combination of satellite observations from CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. The analysis focuses on developing cyclones, to test the model's ability to generate their initial structure. To begin, the extratropical cyclones and their warm fronts are objectively identified and cyclone-local fields are mapped into a vertical transect centered on the surface warm front. To further isolate specific physics, the cyclones are separated using conditional subsetting based on additional cyclone-local variables, and the differences between the subset means are analyzed. Conditional subsets are created based on 1) the transect clouds and 2) vertical motion; 3) the strength of the temperature gradient along the warm front, as well as the storm-local 4) wind speed and 5) precipitable water (PW). The analysis shows that the model does not generate enough frontal cloud, especially at low altitude. The subsetting results reveal that, compared to the observations, the model exhibits a decoupling between cloud formation at high and low altitudes across warm fronts and a weak sensitivity to moisture. These issues are caused in part by the parameterized convection and assumptions in the stratiform cloud scheme that are valid in the subtropics. On the other hand, the model generates proper covariability of low-altitude vertical motion and cloud at the warm front and a joint dependence of cloudiness on wind and PW.

Improving retrievals of regional fine particulate matter concentrations from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) multisatellite observations

A combination of multiplatform satellite observations and statistical data analysis are used to improve the correlation between estimates of PM2.5 (particulate mass with aerodynamic diameter less that 2.5 µm) retrieved from satellite observations and ground-level measured PM2.5. Accurate measurements of PM2.5 can be used to assess the impact of air pollution levels on human health and the environment and to validate air pollution models. The area under study is California's San Joaquin Valley (SJV) that has a history of poor particulate air quality. Attempts to use simple linear regressions to estimate PM2.5 from satellite-derived aerosol optical depth (AOD) have not yielded good results. The period of study for this project was from October 2004 to July 2008 for six sites in the SJV. A simple linear regression between surface-measured PM2.5 and satellite-observed AOD (from MODIS [Moderate Resolution Imaging Spectroradiometer]) yields a correlation coefficient of about 0.17 in this region. The correlation coefficient between the measured PM2.5 and that retrieved combining satellite observations in a generalized additive model (GAM) resulted in an improved correlation coefficient of 0.77. The model used combinations of MODIS AOD, OMI (Ozone Monitoring Instrument) AOD, NO2 concentration, and a seasonal variable as parameters. Particularly noteworthy is the fact that the PM2.5 retrieved using the GAM captures many of the PM2.5 exceedances that were not seen in the simple linear regression model. Implications: Particulate Mass (PM) in the air is a concern because of its effect on climate and human health. PM concentrations retrieved from satellite observations of aerosol optical depth can provide broad regional coverage that is not attained by surface sites. The techniques developed in this paper have resulted in greatly improved correlations between PM retrieved from satellite observations and PM from surface measurements in areas where the correlation is typically low. These improved retrievals can be used to fill in the gaps between surface sites and validate air quality models that are used for air quality forecasts and epidemiological studies. Supplemental Materials: Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for information on the effects of grid size and MODIS data quality flags on the GAM results. The coefficients for the GAMs used in this study are also listed.

Evaluation of ACCMIP outgoing longwave radiation from tropospheric ozone using TES satellite observations

Abstract. We use simultaneous observations of tropospheric ozone and outgoing longwave radiation (OLR) sensitivity to tropospheric ozone from the Tropospheric Emission Spectrometer (TES) to evaluate model tropospheric ozone and its effect on OLR simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES ozone for 2005–2010. These ozone biases have a significant impact on the OLR. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean tropospheric ozone low bias leads up to 120 mW m−2 OLR high bias locally but zonally compensating errors reduce the global OLR high bias to 39 ± 41 m Wm−2 relative to TES data. We show that there is a correlation (R2 = 0.59) between the magnitude of the ACCMIP OLR bias and the deviation of the ACCMIP preindustrial to present day (1750–2010) ozone radiative forcing (RF) from the ensemble ozone RF mean. However, this correlation is driven primarily by models whose absolute OLR bias from tropospheric ozone exceeds 100 m Wm−2. Removing these models leads to a mean ozone radiative forcing of 394 ± 42 m Wm−2. The mean is about the same and the standard deviation is about 30% lower than an ensemble ozone RF of 384 ± 60 m Wm−2 derived from 14 of the 16 ACCMIP models reported in a companion ACCMIP study. These results point towards a profitable direction of combining satellite observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

Global all‐sky shortwave direct radiative forcing of anthropogenic aerosols from combined satellite observations and GOCART simulations

Estimation of aerosol direct radiative forcing (DRF) from satellite measurements is challenging because current satellite sensors do not have the capability of discriminating between anthropogenic and natural aerosols. We combine 3‐hourly cloud properties from satellite retrievals with two aerosol data sets to calculate the all‐sky aerosol direct radiative effect (DRE), which is the mean radiative perturbation due to the presence of both natural and anthropogenic aerosols. The first aerosol data set is based upon Moderate Resolution Imaging Spectroradiometer (MODIS) and Model for Atmospheric Transport and Chemistry (MATCH) assimilation model and is largely constrained by MODIS aerosol optical depth, but it does not distinguish between anthropogenic and natural aerosols. The other aerosol data set is based upon the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model, which does not assimilate aerosol observations but predicts the anthropogenic and natural components of aerosols. Thus, we can calculate the aerosol DRF using GOCART classifications of anthropogenic and natural aerosols and the ratio of DRF to DRE. We then apply this ratio to DRE calculated using MODIS/MATCH aerosols to partition it into DRF (MODIS/MATCH DRF) by assuming that the anthropogenic fractions from GOCART are representative. The global (60°N~60°S) mean all‐sky MODIS/MATCH DRF is −0.51 Wm−2 at the top of the atmosphere (TOA), 2.51 Wm−2 within the atmosphere, and −3.02 Wm−2 at the surface. The GOCART all‐sky DRF is −0.17 Wm−2 at the TOA, 2.02 Wm−2 within the atmosphere, and −2.19 Wm−2 at the surface. The differences between MODIS/MATCH DRF and GOCART DRF are solely due to the differences in aerosol properties, since both computations use the same cloud properties and surface albedo and the same proportion of anthropogenic contributions to aerosol DRE. Aerosol optical depths simulated by the GOCART model are smaller than those in MODIS/MATCH, and aerosols in the GOCART model are more absorbing than those in MODIS/MATCH. Large difference in all‐sky TOA DRF from these two aerosol data sets highlights the complexity in determining the all‐sky DRF, since the presence of clouds amplifies the sensitivities of DRF to aerosol single‐scattering albedo and aerosol vertical distribution.

Volcanic ash over Scandinavia originating from the Grímsvötn eruptions in May 2011

A volcanic ash plume that originated from the eruptions of Iceland's Grímsvötn volcano in May 2011 was observed over the Nordic countries using a combination of satellite observations and ground‐based measurements. The dispersion of the plume was investigated using London VAAC ash forecasts and MODIS observations. Hourly PM10 concentrations at air quality monitoring stations in the southern parts of Norway, Sweden, and Finland exceeded 100 μg/m3 for several hours. The FLEXPART dispersion model has been used to confirm the Icelandic origin of the sampled air masses. Column‐integrated quantities from a Sun photometer and vertical profiles from a Raman lidar were used to estimate the ash concentration within an elevated layer over Stockholm. A lofted layer with an optical thickness of 0.3 at 532 nm passed Stockholm in the morning hours of 25 May 2011. Considering a realistic range of coarse‐mode fractions and specific ash extinctions from the literature, an estimated range of maximum ash mass concentration of 150–340 μg/m3 was derived from the lidar measurements at an altitude of 2.8 km. The lower estimate of the lidar‐derived ash mass concentrations within the planetary boundary layer was found to be in good agreement with surface observations of PM10.

Equatorial refuge amid tropical warming

By combining satellite observations with global climate models, this study provides evidence that a few key equatorial islands and coral atolls could be spared the brunt of previously predicted tropical ocean warming and productivity decline, potentially providing crucial refuges for marine biodiversity amid global climate change.

The influence of burn severity on postfire vegetation recovery and albedo change during early succession in North American boreal forests

Severity of burning can influence multiple aspects of forest composition, carbon cycling, and climate forcing. We quantified how burn severity affected vegetation recovery and albedo change during early succession in Canadian boreal regions by combining satellite observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Canadian Large Fire Database. We used the MODIS‐derived difference Normalized Burn Ratio (dNBR) and initial changes in spring albedo as measures of burn severity. We found that the most severe burns had the greatest reduction in summer MODIS Enhanced Vegetation Index (EVI) in the first year after fire, indicating greater loss of vegetation cover. By 5–8 years after fire, summer EVI for all severity classes had recovered to within 90%–108% of prefire levels. Spring and summer albedo progressively increased during the first 7 years after fire, with more severely burned areas showing considerably larger postfire albedo increases during spring and more rapid increases during summer as compared with moderate‐ and low‐severity burns. After 5–7 years, increases in spring albedo above prefire levels were considerably larger in high‐severity burns (0.20 ± 0.06; defined by dNBR percentiles greater than 75%) as compared to changes observed in moderate‐ (0.16 ± 0.06; for dNBR percentiles between 45% and 75%) or low‐severity burns (0.13 ± 0.06; for dNBR percentiles between 20% and 45%). The sensitivity of spring albedo to dNBR was similar in all ecozones and for all vegetation types along gradients of burn severity. These results suggest carbon losses associated with increases in burn severity observed in some areas of boreal forests may be at least partly offset, in terms of climate impacts, by increases in negative forcing associated with changes in surface albedo.