Moderate-resolution Spectroradiometer Research Articles

Surface Radiative Forcing as a Climate-Change Indicator in North India due to the Combined Effects of Dust and Biomass Burning

This study estimates the radiative forcing by biomass burning and dust aerosols over the Indian subcontinent, with emphasis on the Indo-Gangetic Plains (IGP) during the period from January 2021 to April 2021, based on multiple satellite and reanalysis datasets. In this respect, we used retrievals from the Moderate Resolution Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) system, as well as reanalysis data from the Goddard Earth Observing System, version 5 (GEOS-5), the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), the Copernicus Atmosphere Monitoring Service (CAMS), and ERA-Interim. According to the MERRA-2 and the CAMS, the highest black carbon (BC) concentrations in January 2021 were 7–8 µg m−3, which were significantly lower than measurements performed in main cities along the IGP, such as Patiala, Delhi, and Kanpur. The meteorological data analysis accompanied by the CALIPSO lidar measurements showed that the vertical distribution of total attenuated backscatter (TAB) could reach altitudes of up to ~4–5 km and could be transported over the central Himalayan region. The spatial-averaged daily aerosol radiative forcing (ARF) values over the Indian subcontinent from January 2021 to April 2021 were found to range from −51.40 to −6.08 W m−2 (mean of −22.02 ± 9.19 W m−2), while on a monthly basis, the ARF values varied widely, from −146.24 to −1.63 W m−2 (mean of −45.56 ± 22.85 W m−2) over different parts of the study region. Furthermore, the spatial-averaged daily BC radiative forcing ranged from −2.23 to −0.35 (−1.01 ± 0.40 W m−2), while it varied from −15.29 to −0.31 W m−2 (−2.46 ± 2.32 W m−2) over different regions of southern Asia, indicating a rather small contribution to the total aerosol radiative effect and a large presence of highly scattering aerosols. Our findings highlight the importance of growing biomass burning, in light of recent climate change and the rapid decline in air quality over North India and the Indian Ocean.

The Role of Midlatitude Cyclones in the Emission, Transport, Production, and Removal of Aerosols in the Northern Hemisphere

AbstractWe examine the distribution of aerosol optical depth (AOD) across 27,707 northern hemisphere (NH) midlatitude cyclones for 2005–2018 using retrievals from the Moderate Resolution Spectroradiometer (MODIS) sensor on the Aqua satellite. Cyclone‐centered composites show AOD enhancements of 20%–45% relative to background conditions in the warm conveyor belt (WCB) airstream. Fine mode AOD accounts for 68% of this enhancement annually. Relative to background conditions, coarse mode AOD is enhanced by more than a factor of two near the center of the composite cyclone, co‐located with high surface wind speeds. Within the WCB, MODIS AOD maximizes in spring, with a secondary maximum in summer. Cyclone‐centered composites of AOD from the Modern Era Retrospective analysis for Research and Applications, version 2 Global Modeling Initiative (M2GMI) simulation reproduce the magnitude and seasonality of the MODIS AOD composites and enhancements. M2GMI simulations show that the AOD enhancement in the WCB is dominated by sulfate (37%) and organic aerosol (25%), with dust and sea salt each accounting for 15%. MODIS and M2GMI AOD are 60% larger in North Pacific WCBs compared to North Atlantic WCBs and show a strong relationship with anthropogenic pollution. We infer that NH midlatitude cyclones account for 355 Tg yr−1 of sea salt aerosol emissions annually, or 60% of the 30–80°N total. We find that deposition within WCBs is responsible for up to 35% of the total aerosol deposition over the NH ocean basins. Furthermore, the cloudy environment of WCBs leads to efficient secondary sulfate production.

MODIS sensors can monitor spatiotemporal trends in fog and low cloud cover at 1 km spatial resolution along the U.S. Pacific Coast

Fog and low cloud cover (FLCC) provide critical moisture for ecosystems along the Pacific Coast during the summer months and it is currently unclear how climate change has affected FLCC occurrence. Additionally, FLCC impacts visibility and transportation safety for both road and air traffic. As such, a method for the monitoring of FLCC is necessary to inform land management decisions for fog-obligate species and to improve transportation safety, among other applications. Several gaps exist in current FLCC detection methodologies and while the Moderate Resolution Spectroradiometer (MODIS) sensor has previously been used to detect fog, its utility has not been validated. In this study, we create a 20 year (2000–2020) FLCC dataset using the Terra MODIS cloud flags and examine its effectiveness in detecting daily and monthly FLCC presence along the California and southern Oregon coast for the summer months (June–September). We validate the accuracy of this method using an existing FLCC dataset derived from Geostationary Operational Environmental Satellite (GOES) observations collected at 15 min intervals from 2000 to 2009. The two FLCC datasets have a strong linear relationship for FLCC frequency for each summer month, with an average r2 of 0.82 and p-value of <0.01. This strong relationship demonstrates the ability of Terra MODIS to reliably and accurately detect FLCC. Finally, we demonstrate a case study application of our FLCC dataset in a time series analysis over five coast redwood (Sequoia sempervirens) state parks in the Big Sur region of coastal California. This case study highlights the benefits provided by a 1 km resolution FLCC dataset for ecological applications and for monitoring spatiotemporal FLCC patterns across summer months over two decades. Our case study results showed that the number of foggy days fluctuates considerably year-to-year with no discernible positive or negative trend occurring between 2000 and 2020. Finally, we present a freely-accessible Google Earth Engine application to view and download the monthly FLCC data for all summer months for the years 2000–2020. The methods and dataset presented in this paper provide a means for efficient, daily FLCC monitoring at 1 km2 resolution, as well as the capacity for historical FLCC analyses.

Bayesian Cloud Detection over Land for Climate Data Records

Cloud detection is a necessary step in the generation of land surface temperature (LST) climate data records (CDRs) and affects data quality and uncertainty. We present here a sensor-independent Bayesian cloud detection algorithm and show that it is suitable for use in the production of LST CDRs. We evaluate the performance of the cloud detection with reference to two manually masked datasets for the Advanced Along-Track Scanning Radiometer (AATSR) and find a 7.9% increase in the hit rate and 4.9% decrease in the false alarm rate when compared to the operational cloud mask. We then apply the algorithm to four instruments aboard polar-orbiting satellites, which together can produce a global, 25-year LST CDR: the second Along-Track Scanning Radiometer (ATSR-2), AATSR, the Moderate Resolution Spectroradiometer (MODIS Terra) and the Sea and Land Surface Temperature Radiometer (SLSTR-A). The Bayesian cloud detection hit rate is assessed with respect to in situ ceilometer measurements for periods of overlap between sensors. The consistency of the hit rate is assessed between sensors, with mean differences in the cloud hit rate of 4.5% for ATSR-2 vs. AATSR, 4.9% for AATSR vs. MODIS, and 2.5% for MODIS vs. SLSTR-A. This is important because consistent cloud detection performance is needed for the observational stability of a CDR. The application of a sensor-independent cloud detection scheme in the production of CDRs is thus shown to be a viable approach to achieving LST observational stability over time.

Aerosol optical depth retrieval over land from OCEANSAT-2/ OCM- 2 data – A simple physics based approach

The decoupling of aerosol signals from the at-sensor reflectance measured through a space-borne sensor is a complex task due to the involved coupling mechanism of the interaction of light with the Earth's surface and the atmosphere. Specifically, the retrieval becomes more challenging over the land surface due to appreciable reflectance from the target. This paper presents a simplistic physics-based approach to retrieve Aerosol Optical Depth (AOD) (at a spatial resolution of 0.01°) over the land surface from the top-of-atmosphere (TOA) signals measured from an Indian space-borne sensor, Ocean Color Monitor-2 (onboard OCEANSAT-2 satellite). The estimated AOD from OCM-2 has been compared with that from Moderate Resolution Spectroradiometer (MODIS) onboard AQUA satellite (Collection 6.1 Deep Blue/Dark Target product). The comparison is illustrated for two different conditions prevalent over the Indian region, i.e., smoke-dominated (post-monsoonal) and dust-laden (pre-monsoonal) periods. A case study is also illustrated for the pre- and post-COVID-19 lockdown time period for two different zones of the Indian region. The validation (comparison) of OCM-2 AOD is performed with respect to the data from AERONET stations (MODIS), and we found a correlation coefficient (R2) of 0.84 (0.83) with root mean square error (RMSE) of 0.14 (0.20). Also, ∼87% (70%) of the retrieved OCM-2 (MODIS) AOD values were observed within the expected error (EE) envelope. The sensitivity of the retrieval algorithm with respect to the input parameters is also presented.

Spatio-temporal variability of water use efficiency and its drivers in major forest formations in India

Forests play a significant role in mitigating the effects of climate change and regulating the biogeochemical cycles. Water use efficiency (WUE) is an important indicator that links the carbon and water cycles in terrestrial ecosystems. In the present study, WUE of major forest formations of India was calculated from 2003 to 2018 as the ratio of Moderate Resolution Spectroradiometer (MODIS) Gross Primary Productivity (GPP, MOD17A2H) to evapotranspiration (ET, MOD16A) using the Google Earth Engine platform. The spatial distribution of WUE was mapped and the inter-annual and monthly variations were analysed. The mean annual WUE of forests ranged from 1.78–2.02 gC kgH2O−1. It was observed that tropical thorn forests had the highest WUE (3.52 ± 1.08 gC kgH2O−1) and moist alpine scrub had the lowest WUE (1.05 ± 0.17gC kgH2O−1) among the forest type groups of India. WUE of forests showed an increasing trend with latitude and decreasing trend with elevation. To understand the influence of various bio-meteorological drivers on WUE and their importance in governing these influences, Random Forest (RF) algorithm was used. The bio-meteorological drivers were able to explain 65% of the variability in WUE. Temperature was identified as the most important driver in influencing the WUE of forests. Based on the findings of the study, it can be expected that the global increase in temperature would negatively affect the WUE of the major forest formations of India.

A Machine Learning Approach to Estimate Surface Chlorophyll a Concentrations in Global Oceans From Satellite Measurements

Various approaches have been proposed to estimate surface ocean chlorophyll a concentrations (Chl, mg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> ) from spectral reflectance measured either in the field or from space, each with its own strengths and limitations. Here, we develop a machine learning approach to reduce the impact of spectral noise and improve algorithm performance at the global scale for multiple satellite sensors. Among several candidates, the support vector regression (SVR) approach was found to yield the best algorithm performance as gauged by several statistical measures against field-measured Chl. While statistically the performance of the SVR is slightly worse than the empirical color index (CI) algorithm proposed in Hu et al. (2012) for Chl <; 0.25 mg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , its applicability to global waters is much extended, from the CIs 0.01-0.25 mg m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> (about 75% of the global oceans) to its 0.01-1 mg <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> [about 96% of global oceans according to Sea-viewing Wide Field-of-view Sensor (SeaWiFS) statistics]. Within this range, not only does the SVR show much improved performance over the traditional band-ratio OC x approaches, but the SVR leads to much reduced image noise and much improved cross-sensor consistency between SeaWiFS and Moderate Resolution Spectroradiometer (MODIS)/Aqua and between MODIS/Aqua and Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, compared with the hybrid Ocean CI (OCI) algorithm currently used by the U.S. NASA as the default algorithm for all mainstream ocean color sensors, the SVR avoids the need to merge two different algorithms for intermediate Chl (band subtraction for CI and band ratio for OC x), thus may serve as an alternative approach for global data processing.

Changes in Clear‐Sky Shortwave Aerosol Direct Radiative Effects Since 2002

AbstractA new method for determining clear‐sky shortwave aerosol direct radiative effects (ADRE) from the Clouds and the Earth's Radiant Energy System is used to examine changes in ADRE since 2002 alongside changes in aerosol optical depth (AOD) from the Moderate Resolution Spectroradiometer. At global scales, neither ADRE nor AOD show a significant trend. Over the northern hemisphere (NH), ADRE increases by 0.18 ± 0.17 Wm−2 per decade (less reflection to space) but shows no significant change over the southern hemisphere. The increase in the NH is primarily due to emission reductions in China, the United States, and Europe. The COVID‐19 shutdown shows no noticeable impact on either global ADRE or AOD, but there is a substantial influence over northeastern China in March 2020. In contrast, February 2020 anomalies in ADRE and AOD are within natural variability even though the impact of the shutdown on industry was more pronounced in February than March. The reason is because February 2020 was exceptionally hot and humid over China, which compensated for reduced emissions. After accounting for meteorology and normalizing by incident solar flux, February ADRE anomalies increase substantially, exceeding the climatological mean ADRE by 23%. February and March 2020 correspond to the only period in which adjusted anomalies exceed the 95% confidence interval for 2 consecutive months. Distinct water‐land differences over northeastern China are observed in ADRE but not in AOD. This is likely due to the influence of surface albedo on ADRE in the presence of absorbing aerosols.

The Influence of Seasonal Meteorology on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach Based on Artificial Neural Network and Spatial Analysis

This study aims to develop a hybrid approach based on backpropagation artificial neural network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using aerosol optical depth (AOD) and several meteorological indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using the Motor Vehicle Emission Simulator (MOVES). The measured meteorological variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA’s Moderate Resolution Spectroradiometer (MODIS), respectively. The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150. Coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with R2 = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with R2 = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can reasonably predict the PM2.5 mass and can be used to forecast future trends.

Performance of CCCma and GFDL climate models using remote sensing and surface data for the state of Rio de Janeiro-Brazil

The performance of the CCCma (Canadian Centre for Climate Modelling and Analysis) and GFDL (Geophysical Fluid Dynamic Laboratory) models in the baseline period (1961–2000) and for the future IPCC scenario Representative Concentration Pathways (RCP) 6.0 (2046–2065) were evaluated through the descriptive statistics using spatial interpolation methods (Kriging and Ordinary Co-Kriging) using the exponential, gaussian and spherical spatial models. The daily temperature and rain data from the CCCma and GFDL models were used for the current baseline climate (1961–2000) and the future scenario (2046–2065), data from the National Oceanic and Atmospheric Administration (NOAA) were used for comparisons with the two models and the product MCD12Q1 derived from Moderate resolution spectroradiometer sensor was used to check which areas had higher and lower values of temperature and rain in the State of Rio de Janeiro. For validation, data were compared and geostatistical analysis was performed using Kriging techniques. The averages of air temperature and precipitation showed similar patterns in both models. The CCCma model presented the data closest to the NOAA reanalysis data, with the GFDL model underestimating most precipitation and air temperature data. Through the product MCD12Q1 of the MODIS sensor, it is possible to register marked differences in relation to rain and temperature and their respective land use for the State. For the geostatistics data, it was possible to verify that, for the past and future scenarios, the exponential transitive model presented in the majority of the cases the least degree of spatial dependence (GDE), being therefore considered the best. When comparing the GDE of the two climatic models, it is verified that the CCCma presented the best geostatistical performance and can be used in works to simulate scenarios of future climate changes.

Impact of Aerosol Vertical Distribution on Aerosol Optical Depth Retrieval from Passive Satellite Sensors

When retrieving Aerosol Optical Depth (AOD) from passive satellite sensors, the vertical distribution of aerosols usually needs to be assumed, potentially causing uncertainties in the retrievals. In this study, we use the Moderate Resolution Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as examples to investigate the impact of aerosol vertical distribution on AOD retrievals. A series of sensitivity experiments was conducted using radiative transfer models with different aerosol profiles and surface conditions. Assuming a 0.2 AOD, we found that the AOD retrieval error is the most sensitive to the vertical distribution of absorbing aerosols; a −1 km error in aerosol scale height can lead to a ~30% AOD retrieval error. Moreover, for this aerosol type, ignoring the existence of the boundary layer can further result in a ~10% AOD retrieval error. The differences in the vertical distribution of scattering and absorbing aerosols within the same column may also cause −15% (scattering aerosols above absorbing aerosols) to 15% (scattering aerosols below absorbing aerosols) errors. Surface reflectance also plays an important role in affecting the AOD retrieval error, with higher errors over brighter surfaces in general. The physical mechanism associated with the AOD retrieval errors is also discussed. Finally, by replacing the default exponential profile with the observed aerosol vertical profile by a micro-pulse lidar at the Beijing-PKU site in the VIIRS retrieval algorithm, the retrieved AOD shows a much better agreement with surface observations, with the correlation coefficient increased from 0.63 to 0.83 and bias decreased from 0.15 to 0.03. Our study highlights the importance of aerosol vertical profile assumption in satellite AOD retrievals, and indicates that considering more realistic profiles can help reduce the uncertainties.

The Effect of Droughts on Vegetation Condition in Germany: An Analysis Based on Two Decades of Satellite Earth Observation Time Series and Crop Yield Statistics

Central Europe experienced several droughts in the recent past, such as in the year 2018, which was characterized by extremely low rainfall rates and high temperatures, resulting in substantial agricultural yield losses. Time series of satellite earth observation data enable the characterization of past drought events over large temporal and spatial scales. Within this study, Moderate Resolution Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) 250 m time series were investigated for the vegetation periods of 2000 to 2018. The spatial and temporal development of vegetation in 2018 was compared to other dry and hot years in Europe, like the drought year 2003. Temporal and spatial inter- and intra-annual patterns of EVI anomalies were analyzed for all of Germany and for its cropland, forest, and grassland areas individually. While vegetation development in spring 2018 was above average, the summer months of 2018 showed negative anomalies in a similar magnitude as in 2003, which was particularly apparent within grassland and cropland areas in Germany. In contrast, the year 2003 showed negative anomalies during the entire growing season. The spatial pattern of vegetation status in 2018 showed high regional variation, with north-eastern Germany mainly affected in June, north-western parts in July, and western Germany in August. The temporal pattern of satellite-derived EVI deviances within the study period 2000–2018 were in good agreement with crop yield statistics for Germany. The study shows that the EVI deviation of the summer months of 2018 were among the most extreme in the study period compared to other years. The spatial pattern and temporal development of vegetation condition between the drought years differ.

Evaluating a new algorithm for satellite-based evapotranspiration for North American ecosystems: Model development and validation

We introduce a different operational approach to estimate 8-day average daily evapotranspiration (ET) using both routinely available data and the Penman-Monteith (P-M) equation for canopy transpiration and evaporation of intercepted water and Priestley and Taylor for soil evaporation. Our algorithm considered the environmental constraints on canopy resistance and ET by (1) including vapor pressure deficit (VPD), incoming solar radiation, soil moisture, and temperature constraints on stomatal conductance; (2) using leaf area index (LAI) to scale from the leaf to canopy conductance; and (3) calculating canopy resistance as a function of environmental variables such as net radiation and VPD. Remote sensing data from the Moderate Resolution Spectroradiometer (MODIS) and satellite soil moisture data were used to derive the ET model. The algorithm was calibrated and evaluated using measured ET data from 20 AmeriFlux Eddy covariance flux sites for the period of 2003–2012. We found good agreements between our 8-day ET estimates and observations with mean absolute error (MAE) ranges from 0.17 mm/day to 0.94 mm/day compared with MAE ranging from 0.28 mm/day to 1.50 mm/day for MODIS ET. Compared to MODIS ET, our proposed algorithm has higher correlations and higher Willmott’s index of agreement with observations for the majority of the Ameriflux sites. The strong relationship between the model estimated ET and the flux tower observations implies that our model has the potential to be applied to different ecosystems and at different temporal scales.

Analysis of the Impact of Land Use and Occupation on the Biophysical Variables of the Cerrado Biome in Southwest Goiano, Brazil

The change in the use of natural vegetation by annual or perennial crops, sugarcane and fast-growing forests causes changes in the biophysical variables, and these changes can be monitored by remote sensing. The objective of this work was to evaluate, on a temporal scale, the impacts of land use changes on biophysical variables in the county of Santa Helena de Goias-Goias/Brazil. Between the years of 2000 to 2015 areas were identified for agricultural crops 1 (annual crops), water, agricultural crops 2 (sugarcane), natural vegetation, pasture and urban areas. The MODIS (Moderate Resolution Spectroradiometer) sensor products were selected for study: MOD11A2-Surface temperature; MOD16A2-Real evapotranspiration, MOD13Q1-Enhanced Vegetation Index and rainfall data from TRMM (Tropical Rainfall Measuring Mission). The geographic coordinates referring to the land uses were inserted in the LAPIG platform, searching the information of the biophysical variables referring to the selected pixel. The impact of land use change was evaluated by calculating the weighted average through the quantitative classification of the areas. It is concluded for the period of study that the index of average vegetation of the county had increase. There was an increase in the evapotranspiration volume of the county from 28% from 2000 to 2013 and the average surface temperature of the county showed a reduction of 2 &amp;deg;C in the period from 2000 to 2015.

Dynamic estimating of wetland vegetation cover based on linear spectral mixture and time phase transformation models

ABSTRACTGlobally, remote sensing is being used to monitor vegetation degradation in areas of concern. In recent years, drought and water shortages have caused significant degradation of the wetland vegetation in Zhalong Wetland of Heilongjiang province, China. This paper employed middle- and high-resolution Landsat images to construct a Linear Spectral Mixture Analysis of the wetland, with the end member extraction verified by feasibility analysis and with vegetation cover data extracted over nearly 30 years. By considering the problem of poor timing with middle- and high-resolution images, this paper proposes a phase-transform method that combines the time advantage of moderate-resolution spectroradiometer images with the spatial advantage of high-resolution Landsat imagery. Based on an intensity analysis model, the temporal and spatial characteristics of vegetation cover in the study area were analyzed using a time scale and the level of vegetation cover. The results show that (1) from 1985 to 2015, the vegetation cover showed an overall tendency to degrade, and (2) vegetation cover was extracted based on the phase transformation and linear spectral mixture models with an accuracy of 0.8628, which is higher than that of traditional remote sensing methods. Improving the prediction accuracy in vegetation transfer is of great theoretical value in relation to global climate change.

Moderate Imaging Resolution Spectroradiometer (MODIS) Aerosol Optical Depth Retrieval for Aerosol Radiative Forcing

The present study uses the Aerosol Optical Depth (AOD) retrieved from Moderate Imaging Resolution Spectroradiometer (MODIS) data for the period from January 2011 until December 2015 over an urban area in Kuching, Sarawak. The results show the minimum AOD value retrieved from MODIS is -0.06 and the maximum value is 6.0. High aerosol loading with high AOD value observed during dry seasons and low AOD monitored during wet seasons. Multi plane regression technique used to retrieve AOD from MODIS (AODMODIS) and different statistics parameter is proposed by using relative absolute error for accuracy assessment in spatial and temporal averaging approach. The AODMODIS then compared with AOD derived from Aerosol Robotic Network (AERONET) Sunphotometer (AODAERONET) and the results shows high correlation coefficient (R2) for AODMODIS and AODAERONET with 0.93. AODMODIS used as an input parameters into Santa Barbara Discrete Ordinate Radiative Transfer (SBDART) model to estimate urban radiative forcing at Kuching. The observed hourly averaged for urban radiative forcing is -0.12 Wm−2 for top of atmosphere (TOA), -2.13 Wm−2 at the surface and 2.00 Wm−2 in the atmosphere. There is a moderate relationship observed between urban radiative forcing calculated using SBDART and AERONET which are 0.75 at the surface, 0.65 at TOA and 0.56 in atmosphere. Overall, variation in AOD tends to cause large bias in the estimated urban radiative forcing.

Regional contribution to variability and trends of global gross primary productivity

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000–2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr−1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr−1), and close to the MTE (120 Pg C yr−1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models’ ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.

Contrast in column-integrated aerosol optical properties during heating and non-heating seasons at Urumqi — Its causes and implications

Aerosol optical properties were retrieved from two years' worth of Sunphotometer measurements at Urumqi, an urban station in western China. Distinct seasonal variations of aerosol optical properties were revealed. During the heating season, mean aerosol optical depth at 550nm (τa), Angstrom exponent calculated from aerosol optical depth at wavelength of 440 and 870nm (α) as well as PM2.5 concentration were 0.58±0.33, 1.11±0.34 and 79.5±69.6μgm−3, respectively, which contrasted their counterparts during the non-heating season of 0.32±0.22, 0.79±0.26, and 35.0±20.1μgm−3. Seasonal variations of τa and PM2.5 at Urumqi contrasted with corresponding values in eastern China. Enhancement of τa was associated with fine mode radius (rf) exceeding 0.15μm. Relative humidity frequently exceeded 80% during the heating season, which probably resulted in rf>0.15 as a result of the hygroscopic growth under the humid environment. rf was larger than value assigned to the fine-mode dominated aerosol models used in the dark-target algorithm of the Moderate Resolution Spectroradiometer (MODIS). Annual mean single scattering albedo at 550nm (ω) was 0.87 that was close to the value assigned to the absorption aerosol model in the MODIS algorithm. ω increased as τa increased, probably as a result of the growth of aerosol size. ω of dust aerosols at Urumqi was slightly lower than that in dust source and downwind regions. Substantial differences in aerosol optical and physical properties and their seasonal variation between western and eastern China require maintaining long-term ground based remote sensing aerosols, which would be expected to play an important role in studying aerosol's effects on weather, climate and atmospheric environment.

Inter-comparisons of marine boundary layer cloud properties from the ARM CAP-MBL campaign and two MODIS cloud products.

From April 2009 to December 2010, the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program carried out an observational field campaign on Graciosa Island, targeting the marine boundary layer (MBL) clouds over the Azores region. In this paper, we present an inter-comparison of the MBL cloud properties, namely, cloud liquid water path (LWP), cloud optical thickness (COT) and cloud-droplet effective radius (CER), among retrievals from the ARM mobile facility (AMF) and two Moderate Resolution Spectroradiometer (MODIS) cloud products (GSFC-MODIS and CERES-MODIS). A total of 63 daytime single-layer MBL cloud cases are selected for inter-comparison. Comparison of collocated retrievals indicates that the two MODIS cloud products agree well on both COT and CER retrievals, with the correlation coefficient R>0.95. despite their significant difference in spatial sampling. In both MODIS products, the CER retrievals based on the 2.1 µm band (CER2.1) is significantly smaller than that based on the 3.7 µm band (CER3.7). The GSFC-MODIS cloud product is collocated and compared with ground-based ARM observations at several temporal-spatial scales. In general, the correlation increases with more precise collocation. For the 63 selected MBL cloud cases, the GSFC-MODIS LWP and COT retrievals agree reasonably well with the ground-based observations with no apparent bias and correlation coefficient R around 0.85 and 0.70, respectively. However, GSFC-MODIS CER3.7 and CER2.1 retrievals have a lower correlation (R~0.5) with the ground-based retrievals. For the 63 selected cases, they are on average larger than ground observations by about 1.5 µm and 3.0 µm, respectively. Taking into account that the MODIS CER retrievals are only sensitive to cloud top reduces the bias only by 0.5 µm.

Study of thermal IR phenomena associated with 27 February 2010 Chile Mw 8.8 earthquake using MODIS data

Stresses acting on fault systems before major earthquakes can produce thermal anomalies; these abnormalities can be observed using multi-sensor satellite data. Moderate resolution spectro-radiometer on board the terra and aqua satellites can provide thermal infrared (TIR) imaging data for land and ocean. These TIR data have recorded short-lived thermal anomaly prior to major earthquakes. It is suggested by others that these electromagnetic (EM) phenomena relate to stress build up before earthquakes. The objective of this study is to find an association between spatial extent and temporal evolution of thermal anomalies and known major earthquakes near the boundary of Nazca plate and South American plate. Our approach is to map the TIR transient fields from polar orbiting satellites and analysing those data using time series temperature plots to detect the abnormal thermal trends before the earthquake. This study concentrated on marine earthquakes to detect the changes in both land and ocean before seismic activity.