Combination Of Satellite Observations Research Articles

Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective

A growing literature documents the effects of heat stress on premature mortality and other adverse health outcomes. Urban heat islands (UHI) can exacerbate these adverse impacts in cities by amplifying heat exposure during the day and inhibiting the body’s ability to recover at night. Since the UHI intensity varies not only across, but also within cities, intra-city variation may lead to differential impact of urban heat stress on different demographic groups. To examine these differential impacts, we combine satellite observations with census data to evaluate the relationship between distributions of both UHI and income at the neighborhood scale for 25 cities around the world. We find that in most (72%) cases, poorer neighborhoods experience elevated heat exposure, an incidental consequence of the intra-city distribution of income in cities. This finding suggests that policymakers should consider designing city-specific UHI reduction strategies to mitigate its impacts on the most socioeconomically vulnerable populations who may be less equipped to adapt to environmental stressors. Since the strongest contributor of intra-urban UHI variability among the physical characteristics considered in this study is a neighborhood’s vegetation density, increasing green space in lower income neighborhoods is one strategy urban policymakers can adopt to ameliorate some of UHI’s inequitable burden on economically disadvantaged residents.

QUANTITATIVE ASSESSMENT OF RICE CROP DAMAGE POST TITLI CYCLONE IN SRIKAKULAM, ANDHRA PRADESH USING GEO-SPATIAL TECHNIQUES

Abstract. Mapping the extent of damage due to natural calamities remains one of the thrust areas in monitoring resource inventory through geo-spatial techniques. The effect of the cyclone ‘Titli’ and heavy rains during first fortnight of October 2018 in Srikakulam district, Andhra Pradesh State has been demonstrated using geo-spatial technology in terms of flood inundated rice area and corresponding yield and production loss. The pre- and post-cyclone (5 and 13 October 2018) flood inundation maps were generated using Sentinel-1A and TerraSAR-X Synthetic Aperture Radar (SAR) data respectively. The pre-cyclone rice area estimates were derived from multi-temporal Sentinel-1A SAR data, while yield forecast is based on the combination of satellite observations and yield simulation using ORYZA crop growth model. An intensive ground truth data collection had been carried out for the validation of satellite-derived rice area estimation of pre-cyclone event. An accuracy assessment has been carried out for district, mandal and village level. An overall accuracy of 96% with kappa coefficient 0.92 has been achieved. With the help map flood inundation and rice area maps, mandal-wise flood affected rice area and corresponding yield loss have been estimated. The post-cyclone ground truth data had been collected for quantitative assessment of crop damaged area. An overall accuracy of the flood affected rice map was 85% with kappa coefficient 0.70. It was estimated that rice crop damage assessment with SAR data indicated 53312 ha out of 205174 ha were affected and corresponding estimated yield as well as production are 0.8 t/ha and 189160 t respectively.

Anthropogenic Aerosol Pollution over the Eastern Slope of the Tibetan Plateau

In this study, a combination of satellite observations and reanalysis datasets is used to analyze the spatiotemporal distribution, classification and source of pollutants over the eastern slope of the Tibetan Plateau (ESTP). The aerosol optical depth (AOD) over the ESTP is extremely large and even larger than some important industrialized regions and deserts. The main aerosol component over the ESTP is sulfate, followed by carbonaceous and dust aerosols. Local emissions related to human activity directly contribute to the accumulation of sulfate and carbonaceous aerosols over the Sichuan Basin. In addition, in spring, abundant carbonaceous aerosols emitted from forest, grassland and savanna fires in Southeast Asia can be transported by the prevailing southwesterly wind to southern China and the ESTP. The dust AOD over the ESTP peaks in spring because of the transport from the Taklimakan and Gobi deserts. Additionally, the high aerosol loading over the ESTP is also directly related to the meteorological background. Due to the special topography, the terrain-driven circulation can trap aerosols in the Sichuan Basin and these aerosols can climb along the ESTP due to the perennial updraft. The aerosol loading is lowest in summer because of effective wet deposition induced by the strong precipitation and better dispersion conditions due to the larger vertical temperature gradients and ascending air movement enhanced by the plateau heat pump effect. In contrast, the aerosol loading is greatest in winter. Abundant anthropogenic aerosols over the ESTP may generate some climatic and environmental risks and consequently greatly influence the downstream regions.

Polar Cyclones at the Origin of the Reoccurrence of the Maud Rise Polynya in Austral Winter 2017

AbstractThis study examines the role of atmospheric forcings in the occurrence of open‐ocean polynyas by investigating the case of the austral winter 2017's polynya located in the Lazarev Sea sector to the east of the Weddell Sea, known as the Maud Rise polynya or the Weddell Polynya. The ice‐free zone appeared in mid‐September 2017 and grew to as large as 80,000 km2 by the end of October 2017 before merging with the open ocean after the sea ice started to retreat at the beginning of the austral summer. Using a combination of satellite observations and reanalysis data at high spatiotemporal resolution, we found that severe cyclones, occurring over the ice pack, have a deterministic role in creating strong divergence in the sea ice field through strong cyclonic surface winds leading to the opening of the polynya. The occurrence of intense and frequent cyclones over the ice pack during austral winter 2017 was unusual, and it occurred under an enhanced strong positive meridional transport of heat flux and moisture toward Antarctica associated with an amplification of the atmospheric zonal wave 3 and a strong positive Southern Annular Mode index. We found that the opening of the polynya was not primarily due to direct ice melt by thermodynamic effects but rather to strong dynamical forcing by the winds on the sea ice, as in the case of coastal polynyas. Indeed, the meridional transport of heat toward Antarctica occurred over the Weddell Sea sector (i.e., to the east of the Lazarev Sea sector where the polynya is located) whereas the Lazarev Sea sector was under the influence of equatorward transport of cold air masses at that time. Our results show that the supply of warm and moist air coming from the west side of the South Atlantic Ocean into the Weddell Sea significantly increased the potential for cyclone formation as measured by the Eady growth rate leading to intense and frequent cyclogenesis over the ice pack, far south from the ice edge. After cyclogenesis in the Weddell Sea, these cyclones intensified as they moved eastward spinning over the Lazarev Sea with intensity comparable to category 11—violent storms—in the Beaufort scale. The cyclonic winds generated sea ice divergence by pushing the ice away from the cyclone center: To the east, north of it and to the west, south of it, which led to the reoccurrence of the Maud Rise polynya in mid‐September 2017.

Source Identification of Episodic Rain Pollutants by New Approach: Combining Satellite Observations and Backward Air Mass Trajectories

ABSTRACT In this study, various source apportionment methods, viz., enrichment factor, relative source contribution, and factor analysis in addition to cluster analysis coupled with satellite observations, were used to investigate the origin of rainwater pollutants in an arid urban area of Saudi Arabia. The rainwater samples were collected by an automatic wet-only sequential rain sampler and analyzed for their pH, electrical conductivity (EC), and major ions (Cl–, NO3–, SO42–, HCO3–, Na+, K+, NH4+, Ca2+, and Mg2+). The results revealed that the chemical composition was dominated by Ca2+, SO42–, Cl–, HCO3–, and Na+, which represented 80% of the total ionic equivalent concentration. Ca2+, the most abundant ion, exhibited a mean concentration of 480 µeq L–1 and accounted for 30% of the total ionic equivalent concentration. The study area received a total ionic wet deposition flux (FWD) of 4.07 tons km–2 y–1, and SO42– and NO3– contributed 76% and 24%, respectively, of the rainwater acidity. Ca2+ was responsible for 80% of the rainwater’s neutralization. The source apportionment indicated that intense local human activity and medium- to long-range transport from Kuwait, Iraq, Qatar, Riyadh, and the Western Province produced the majority of the anthropogenic components (SO42–, NO3–, and NH4+), which represented 47% of the total ionic FWD. However, moderate to high particulate matter loads over the southern part of Saudi Arabia and long-range transport from Iraq and Kuwait contributed the crustal components (Ca2+, HCO3–, K+, and Mg2+), which accounted for 36% of the total ionic FWD, whereas the Arabian Gulf was the primary source of the marine components (Na+ and Cl–). We found combining satellite observations and cluster analysis of backward air mass trajectories to be an effective new approach for assessing the source-receptor relationships for atmospheric air pollutants.

Retrieving Ocean Subsurface Temperature Using a Satellite‐Based Geographically Weighted Regression Model

AbstractAccurately retrieving and describing subsurface temperature on a large scale can provide valuable information that can be used for subsurface dynamic and variability studies. This study develops a new satellite‐based geographically weighted regression (GWR) model to estimate a subsurface temperature anomaly (STA) in the upper 2,000 m of the Indian Ocean by combining satellite observations (sea surface height, sea surface temperature, sea surface salinity, and sea surface wind) and Argo in situ data (STA). This model improves the estimation accuracy by considering the significant spatial nonstationarity feature between the surface and subsurface parameters in the ocean. The performance of the GWR model is measured by using Akaike Information Criterion combined with root‐mean‐square error and R2. The results showed that the proposed GWR model can easily retrieve the STA and outperform the ordinary least squares model. The GWR model can also explain the contribution from each variable via a local regression coefficient distribution. The sea surface height from altimetry is the most significant variable for GWR estimation. This study demonstrates the great potential and advantage of the GWR model for large‐scale subsurface modeling and information retrieving. Thus, we have developed a novel approach for investigating subsurface thermal anomaly and variability from satellite observations.

Mass balance of the Antarctic Ice Sheet from 1992 to 2017.

The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992-2017 (5±46 billion tonnes per year) being the least certain.

Increasingly Important Role of Atmospheric Aridity on Tibetan Alpine Grasslands

AbstractPronounced warming occurring on the Tibetan Plateau is expected to stimulate alpine grassland growth but could also increase atmospheric aridity that limits photosynthesis. But there lacks a systematic assessment of the impact of atmospheric aridity on alpine grassland productivity. Here we combine satellite observations, flux‐tower‐based productivity, and model simulations to quantify the effect of atmospheric aridity on grassland productivity and its temporal change between 1982 and 2011. We found a negative impact of atmospheric vapor pressure deficit on grassland productivity. This negative effect becomes increasingly intensified in terms of the impact severity and extent, suggesting an increasingly important role of atmospheric aridity on productivity. We further demonstrated that this negative effect is mitigated but cannot be overcompensated by the positive effect of rising CO2. Given that vapor pressure deficit is projected to further increase by ~10–38% in the future, Tibetan alpine grasslands will face an increasing stress of atmospheric drought.

Impact of deforestation and climate on the Amazon Basin\u2019s above-ground biomass during 1993\u20132012

Since the 1960s, large-scale deforestation in the Amazon Basin has contributed to rising global CO2 concentrations and to climate change. Recent advances in satellite observations enable estimates of gross losses of above-ground biomass (AGB) stocks due to deforestation. However, because of simultaneous regrowth, the net contribution of deforestation emissions to rising atmospheric CO2 concentrations is poorly quantified. Climate change may also reduce the potential for forest regeneration in previously disturbed regions. Here, we address these points of uncertainty with a machine-learning approach that combines satellite observations of AGB with climate data across the Amazon Basin to reconstruct annual maps of potential AGB during 1993–2012, the above-ground C storage potential of the undisturbed landscape. We derive a 2.2 Pg C loss of AGB over the study period, and, for the regions where these losses occur, we estimate a 0.7 Pg C reduction in potential AGB. Thus, climate change has led to a decline of ~1/3 in the capacity of these disturbed forests to recover and recapture the C lost in disturbances during 1993–2012. Our approach further shows that annual variations in land use change mask the natural relationship between the El Niño/Southern Oscillation and AGB stocks in disturbed regions.

The implications of dust ice nuclei effect on cloud top temperature in a complex mesoscale convective system

Mineral dust is the most important natural source of atmospheric ice nuclei (IN) which may significantly mediate the properties of ice cloud through heterogeneous nucleation and lead to crucial impacts on hydrological and energy cycle. The potential dust IN effect on cloud top temperature (CTT) in a well-developed mesoscale convective system (MCS) was studied using both satellite observations and cloud resolving model (CRM) simulations. We combined satellite observations from passive spectrometer, active cloud radar, lidar, and wind field simulations from CRM to identify the place where ice cloud mixed with dust particles. For given ice water path, the CTT of dust-mixed cloud is warmer than that in relatively pristine cloud. The probability distribution function (PDF) of CTT for dust-mixed clouds shifted to the warmer end and showed two peaks at about −45 °C and −25 °C. The PDF for relatively pristine cloud only show one peak at −55 °C. Cloud simulations with different microphysical schemes agreed well with each other and showed better agreement with satellite observations in pristine clouds, but they showed large discrepancies in dust-mixed clouds. Some microphysical schemes failed to predict the warm peak of CTT related to heterogeneous ice formation.

Observation-based study on aerosol optical depth and particle size in partly cloudy regions.

This study seeks to help better understand aerosol-cloud interactions by examining statistical relationships between aerosol properties and nearby low-altitude cloudiness using satellite data. The analysis of a global dataset of MODIS (Moderate Resolution Imaging Spectroradiometer) observations reveals that the positive correlation between cloudiness and aerosol optical depth (AOD) reported in earlier studies is strong throughout the globe and during both winter and summer. Typically, AOD is 30-50% higher on cloudier-than-average days than on less cloudy days. A combination of satellite observations and MERRA-2 global reanalysis data reveals that the correlation between cloud cover and AOD is strong for all aerosol types considered: sulfate, dust, carbon, and sea salt. The observations also indicate that in the presence of nearby clouds, aerosol size distributions tend to shift toward smaller particles over large regions of the Earth. This is consistent with a greater cloud-related increase in the AOD of fine mode than of coarse mode particles. The greater increase in fine mode AOD implies that the cloudiness-AOD correlation does not come predominantly from cloud detection uncertainties. Additionally, the results show that aerosol particle size increases near clouds even in regions where it decreases with increasing cloudiness. This suggests that the decrease with cloudiness comes mainly from changes in large-scale environment, rather than from clouds increasing the number or the size of fine mode aerosols. Finally, combining different aerosol retrieval algorithms demonstrated that quality assessment flags based on local variability can help identifying when the observed aerosol populations are affected by surrounding clouds.

An integrated approach to monitoring ecosystem services and agriculture: implications for sustainable agricultural intensification in Rwanda.

Maintaining the long-term sustainability of human and natural systems across agricultural landscapes requires an integrated, systematic monitoring system that can track crop productivity and the impacts of agricultural intensification on natural resources. This study presents the design and practical implementation of a monitoring framework that combines satellite observations with ground-based biophysical measurements and household surveys to provide metrics on ecosystem services and agricultural production at multiple spatial scales, reaching from individual households and plots owned by smallholder farmers to 100-km2 landscapes. We developed a set of protocols for monitoring and analyzing ecological and agricultural household parameters within two 10×10-km landscapes in Rwanda, including soil fertility, crop yield, water availability, and fuelwood sustainability. Initial results suggest providing households that rely on rainfall for crop irrigation with timely climate information and improved technical inputs pre-harvest could help increase crop productivity in the short term. The value of the monitoring system is discussed as an effective tool for establishing a baseline of ecosystem services and agriculture before further change in land use and climate, identifying limitations in crop production and soil fertility, and evaluating food security, economic development, and environmental sustainability goals set forth by the Rwandan government.

Citizen Bio-Optical Observations from Coast- and Ocean and Their Compatibility with Ocean Colour Satellite Measurements

Marine processes are observed with sensors from both the ground and space over large spatio-temporal scales. Citizen-based contributions can fill observational gaps and increase environmental stewardship amongst the public. For this purpose, tools and methods for citizen science need to (1) complement existing datasets; and (2) be affordable, while appealing to different user and developer groups. In this article, tools and methods developed in the 7th Framework Programme of European Union (EU FP 7) funded project Citclops (citizens’ observatories for coast and ocean optical monitoring) are reviewed. Tools range from a stand-alone smartphone app to devices with Arduino and 3-D printing, and hence are attractive to a diversity of users; from the general public to more specified maker- and open labware movements. Standardization to common water quality parameters and methods allows long-term storage in regular marine data repositories, such as SeaDataNet and EMODnet, thereby providing open data access. Due to the given intercomparability to existing remote sensing datasets, these tools are ready to complement the marine datapool. In the future, such combined satellite and citizen observations may set measurements by the engaged public in a larger context and hence increase their individual meaning. In a wider sense, a synoptic use can support research, management authorities, and societies at large.

Combining satellite observations to develop a global soil moisture product for near-real-time applications

Abstract. The soil moisture dataset that is generated via the Climate Change Initiative (CCI) of the European Space Agency (ESA) (ESA CCI SM) is a popular research product. It is composed of observations from 10 different satellites and aims to exploit the individual strengths of active (radar) and passive (radiometer) sensors, thereby providing surface soil moisture estimates at a spatial resolution of 0.25°. However, the annual updating cycle limits the use of the ESA CCI SM dataset for operational applications. Therefore, this study proposes an adaptation of the ESA CCI product for daily global updates via satellite-derived near-real-time (NRT) soil moisture observations. In order to extend the ESA CCI SM dataset from 1978 to present we use NRT observations from the Advanced Scatterometer on-board the two MetOp satellites and the Advanced Microwave Scanning Radiometer 2 on-board GCOM-W. Since these NRT observations do not incorporate the latest algorithmic updates, parameter databases and intercalibration efforts, by nature they offer a lower quality than reprocessed offline datasets. In addition to adaptations of the ESA CCI SM processing chain for NRT datasets, the quality of the NRT datasets is a main source of uncertainty. Our findings indicate that, despite issues in arid regions, the new CCI NRT dataset shows a good correlation with ESA CCI SM. The average global correlation coefficient between CCI NRT and ESA CCI SM (Pearson's R) is 0.80. An initial validation with 40 in situ observations in France, Spain, Senegal and Kenya yields an average R of 0.58 and 0.49 for ESA CCI SM and CCI NRT, respectively. In summary, the CCI NRT product is nearly as accurate as the existing ESA CCI SM product and, therefore, of significant value for operational applications such as drought and flood forecasting, agricultural index insurance or weather forecasting.

Variability of fire carbon emissions in equatorial Asia and its nonlinear sensitivity to El Niño

AbstractThe large peatland carbon stocks in the land use change‐affected areas of equatorial Asia are vulnerable to fire. Combining satellite observations of active fire, burned area, and atmospheric concentrations of combustion tracers with a Bayesian inversion, we estimated the amount and variability of fire carbon emissions in equatorial Asia over the period 1997–2015. Emissions in 2015 were of 0.51 ± 0.17 Pg carbon—less than half of the emissions from the previous 1997 extreme El Niño, explained by a less acute water deficit. Fire severity could be empirically hindcasted from the cumulative water deficit with a lead time of 1 to 2 months. Based on CMIP5 climate projections and an exponential empirical relationship found between fire carbon emissions and water deficit, we infer a total fire carbon loss ranging from 12 to 25 Pg by 2100 which is a significant positive feedback to climate warming.

Global observational diagnosis of soil moisture control on the land surface energy balance

AbstractAn understanding of where and how strongly the surface energy budget is constrained by soil moisture is hindered by a lack of large‐scale observations, and this contributes to uncertainty in climate models. Here we present a new approach combining satellite observations of land surface temperature and rainfall. We derive a Relative Warming Rate (RWR) diagnostic, which is a measure of how rapidly the land warms relative to the overlying atmosphere during 10 day dry spells. In our dry spell composites, 73% of the land surface between 60°S and 60°N warms faster than the atmosphere, indicating water‐stressed conditions, and increases in sensible heat. Higher RWRs are found for shorter vegetation and bare soil than for tall, deep‐rooted vegetation, due to differences in aerodynamic and hydrological properties. We show how the variation of RWR with antecedent rainfall helps to identify different evaporative regimes in the major nonpolar climate zones.

Estimation of evaporation over the upper Blue Nile basin by combining observations from satellites and river flow gauges

AbstractReliable estimates of regional evapotranspiration are necessary to improve water resources management and planning. However, direct measurements of evaporation are expensive and difficult to obtain. Some of the difficulties are illustrated in a comparison of several satellite‐based estimates of evapotranspiration for the Upper Blue Nile (UBN) basin in Ethiopia. These estimates disagree both temporally and spatially. All the available data products underestimate evapotranspiration leading to basin‐scale mass balance errors on the order of 35 percent of the mean annual rainfall. This paper presents a methodology that combines satellite observations of rainfall, terrestrial water storage as well as river‐flow gauge measurements to estimate actual evapotranspiration over the UBN basin. The estimates derived from these inputs are constrained using a one‐layer soil water balance and routing model. Our results describe physically consistent long‐term spatial and temporal distributions of key hydrologic variables, including rainfall, evapotranspiration, and river‐flow. We estimate an annual evapotranspiration over the UBN basin of about 2.55 mm per day. Spatial and temporal evapotranspiration trends are revealed by dividing the basin into smaller subbasins. The methodology described here is applicable to other basins with limited observational coverage that are facing similar future challenges of water scarcity and climate change.

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.