nasa-gesdisc/es-publications-researchareas huggingface dataset:
Black carbon and mineral dust on two glaciers on the central Tibetan Plateau: sources and implications (2020)

Sources and implications of black carbon (BC) and mineral dust (MD) on two glaciers on the central Tibetan Plateau were estimated based on in situ measurements and modeling. The results indicated that BC and MD accounted for ~11  1% and 4  0% of the albedo reduction relative to clean snow, while the radiative forcing varied between 11 and 196 and 189 W m2, respectively. Assessment of BC and MD contributions to the glacier melt can reach up 88 to 434 and 35 to 187 mm w.e., respectively, contributing ~923 and 410% of the total glacier melt. A footprint analysis indicated that BC and MD deposited on the glaciers originated mainly from the Middle East, Central Asia, North China and South Asia during the study period. Moreover, a potentially large fraction of BC may have originated from local and regional fossil fuel combustion. This study suggests that BC and MD will enhance glacier melt and provides a scientific basis for regional mitigation efforts.

Research areas: Cryospheric Indicators, Energy Production/Use
Fish spawning strategies in the northwestern Mediterranean Sea (2019)

The Mediterranean is globally considered an oligotrophic sea.
However, there are some places or certain seasons in which mechanisms
that enhance fertility may occur. These mechanisms, and related
processes, are especially relevant in maintaining fish populations
when they take place during the period of larval development. This
contribution analyzes how environmental conditions occurring
in the NW Mediterranean, at local and seasonal scales, determine
the temporal and spatial patterns of fish reproductive activity in
the region. The structure of the bathymetry, diversity of adult fish
habitats and hydrodynamic mechanisms conditioning the primary
production of the region (e.g., shelf-slope density front and associated
current, continental water inflows, winter mixing, stratification
of the water column) determine the location of spawning and the
distribution patterns of fish eggs and larvae. A pronounced seasonal
variability regarding both the number of species and the number
of fish larvae in the plankton can be observed throughout the year.
Most NW Mediterranean fish reproduce during the springsummer
stratification period, when the phytoplankton biomass values at the
upper layers of the water column are lower than in winter. The
development of the Deep Chlorophyll Maximum in this period and
the high zooplankton biomass associated to it offers an important food
source for the larvae. Additionally, during this period the inputs of
continental waters are one of the fertilization mechanisms of surface
waters and some species, as anchovy, takes advantage of this situation.
Autumnwinter is the period with lower ichthyoplankton diversity,
being dominated by sardine. Vertical mixing during winter is one
of the mechanisms that enhance productivity. Overall, fish species
show reproductive strategies and larval fish behavior that allow them
to take advantage of the available resources throughout the seasonal
cycle. These strategies, together with the high ecological efficiency
of oligotrophic systems, contribute to the relatively high yield of
Mediterranean fisheries. In a context of global change, understanding
of the mechanisms relating environmental changes to the extent of
spatial and temporal location of suitable spawning habitats of fish is
a key first step to predicting and projecting such future changes, and
thereby adapting to these changes.

Research areas: Agriculture, Ecosystems
Absorbing aerosol influence on temperature maxima: An observation based study over India (2020)

Increasing trends in summer-time temperature maxima (Tmax) over India, show consequent increases in the intensity and frequency of heatwave events in recent years. Heat waves have been largely attributed to large-scale meteorological blocking, characterized by subsidence, clear skies and low soil moisture, in observational studies, or greenhouse gas enhancements in model studies. While radiative effects of absorbing aerosols are acknowledged, the association of absorbing aerosols with temperature maxima has not been investigated comprehensively. In the current study, statistical tools (such as correlation and Granger causality) were applied to long term (19792013) satellite and ground based observations to evaluate influence of absorbing aerosols on Tmax in north-west India (Tmax-NW). Regional absorbing aerosol index (AAI) in the north-west (AAI-NW) and central-India (AAI-CI) showed co-variability with Tmax-NW, implying connections to both local and non-local absorbing aerosols. The effects persisted on seasonal and heatwave event scales, becoming stronger on heatwave days with presence of enhanced AAI loadings. Causal effects of AAI-NW and AAI-CI were identified on Tmax-NW with a lag of 111 days, across multiple years, thereby establishing the influence of absorbing aerosols on heatwave events. The absence of confounding effects of surface pressure on these links suggests that, even during heat wave events linked to atmospheric blocking, absorbing aerosols can further enhance temperature maxima and related heatwave intensity.

Research areas: Heat
Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil (2020)

This study analyzes five years of 1-min solar global horizontal irradiance (GHI) and direct normal irradiance (DNI) observations obtained at Petrolina (northeast Brazil). Quality-assured hourly and daily averages are obtained after applying filters and methodologies based on a Baseline Solar Radiation Network (BSRN) quality-control procedure. To calculate correct hourly averages, a minimum fraction of 20% of valid GHI or DNI minutely data is needed, as well as at least 60% of valid days to calculate correct daily-mean monthly values. An asymmetric diurnal pattern is found in GHI and DNI during all months, attributed to consistently higher cloudiness in the morning. The quality-assured hourly and monthly-mean GHI and DNI time series are compared to estimates from 11 solar databases regularly used in solar resource assessment studies: CAMS, CERES, ERA5, INPE, MERRA-2, Meteonorm, NASA-POWER, NSRDB, SARAH, SWERA-BR, and SWERA-US. For hourly GHI values, a range of RMS differences is found between the best (CAMS, 17.3%) and the worst (MERRA-2, 38.9%) results. The latter database is also affected by a larger bias (18.7%) than CAMS (4%). Larger RMS differences are found with hourly DNI, in a range extending from 37% (CAMS) to 63.4% (ERA5). Biases are all above 12%, except for CERES (1%). For long-term mean-monthly GHI results, low biases of less than 1% are obtained with CAMS, CERES and NASA-POWER, whereas MERRA-2 overestimates (13%). Larger biases are found for mean-monthly DNI, spanning between CAMS (3%) and Meteonorm (18.4%). Overall, CAMS appears the most consistent solar database for long-term irradiance time series at Petrolina. The significant differences found here between modeled databases are larger than expected, and underline the importance of regional validation studies like this one to decrease the incidence of uncertainties in solar resource assessments on the design and performance of solar energy projects.

Research areas: Sun-Earth Interactions, Energy Production/Use
Performance of major mechanized rainfed agricultural production in Sudan: Sorghum vulnerability and resilience to climate since 1970 (2019)

Food security in the African Sahel has frequently been threatened by climate variability and change. A large part of the farming system in the arid and semi-arid region of Sudan is heavily rainfed, with sorghum representing the major crop. This study sheds light on the performance of sorghum production in the major mechanized sector in Sudan since 1970. Despite a significant extensification of the mechanized cultivated area of sorghum, a drastic loss of area at harvest took place during 19702016. The sorghum production showed both increases and decreases in the average over time besides high year-to-year variability. Synchronous with the above findings, a significant steady decline of sorghum yield occurred from the 1970s level (744.3 kg/ha) to 476.6 kg/ha since 1982. Based on the ratio of growing season precipitation to reference evapotranspiration, a Standardized Multi-Criteria Drought Index (SMCDI) for the period 19412015 was introduced using three drought characteristics, namely drought severity, dry spell and time relative frequency of drought. There occurred 23 mild to extreme droughts and 17 mild to extreme wet cases over the period 1970-2015. Aggregation of the SMCDI with climatic sorghum yield revealed highly variable performance of this crop. Accordingly, 26 vulnerable and 20 resilient sorghum yield years were discernible under drought, near normal or wet conditions due to a combination of factors not only climate, i.e. physio-geographic, socio-economic and institutional factors. Apart from the regional analysis, one representative state was also selected for further in-depth spatio-temporal assessment of the SMCDI, integrated Normalized Difference Vegetation Index (iNDVI), Modified Vegetation Condition Index (MVCI), sorghum yield, precipitation and reference evapotranspiration data over the period 20012014. The results also underscored the spatio-temporal and local-scale vulnerability of the farming system under study to climate variability. Such findings urge for interventions in the mechanized rainfed sorghum farming system to enable improvement of its performance, drought vulnerability reduction, development of alternatives, and achievement of food security.

Research areas: Agriculture, Droughts
Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction (2019)

The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan, regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization, monitoring and conversion of renewable energy into usable power. Data-driven models that investigate the feasibility of solar-fueled energies, face challenges in respect to identifying their appropriate input data as such variables may not be available at all sites due to a lack of environmental monitoring system. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived predictors are employed to train three-phase hybrid SVR model for monthly GSR prediction. Firstly, to acquire relevant model input features, MODIS variables are screened with the Particle Swarm Optimization (PSO) algorithm, and secondly, a Gaussian emulation method of sensitivity analysis is incorporated on all screened variables to ascertain their relative role in predicting GSR. To address pertinent issues of non-stationarities, PSO selected variables are decomposed with Maximum Overlap Discrete Wavelet Transformation prior to its incorporation in Support Vector Regression (SVR), constructing a three-phase PSO-W-SVR hybrid model where the hyper-parameters are acquired by evolutionary (i.e., PSO & Genetic Algorithm) and Grid Search methods. Three-phase PSO-W-SVR hybrid model is benchmarked with alternative machine learning models. Thirty-nine model scenarios are formulated: 13 without feature selection (e.g., SVR), 13 with feature selection (e.g., PSO-SVR for two-phase models) and the remainder 13 with feature selection strategy coupled with data decomposition algorithm (e.g., PSO-W-SVR leading to a three-phase model). Metrics such as skill score (RMSESS), root mean square error (RMSE), mean absolute error (MAE), Willmotts (WI), Legates & McCabes (E1) and NashSutcliffe coefficients (ENS) are applied to comprehensively evaluate prescribed models. Empirical results register high performance of three-phase hybrid PSO-W-SVR models, exceeding the prescribed alternative models. High predictive ability evidenced by a low RRMSE and high E1 ascertains PSO-W-SVR hybrid model as considerably favorable in its capability to be enriched by MODIS satellite-derived variables. Maximum Overlap Discrete Wavelet Transform algorithm is also seen to provide resolved patterns in satellite variables, leading to a superior performance compared to the other data-driven model. The research avers that a three-phase hybrid PSO-W-SVR model can be a viable tool to predict GSR using satellite derived data as predictors, and is particularly useful for exploration of renewable energies where satellite footprint are present but regular environmental monitoring systems may be absent.

Research areas: Sun-Earth Interactions, Energy Production/Use
Atmospheric anomalies associated with Mw>6.0 earthquakes in Pakistan and Iran during 20102017 (2019)

Recent advances on satellite-based measurement of different atmospheric constituents at different heights provide sufficient evidences of short and long-term earthquake (EQ) precursors. In this paper, atmospheric anomalies are investigated related to three large magnitude Mw > 6.0 EQs in Pakistan and Iran during 20102017 (i.e., January 18, 2011 (Mw 7.2, Southwestern Pakistan), April 16, 2013 (Mw 7.7, East of Khash Iran) and February 07, 2017 (Mw 6.3, Pasni, Pakistan)). For this purpose, satellite based Outgoing Longwave Radiation (OLR), Surface Temperature (ST), Aerosol Optical Depth (AOD) and NO2 are investigated by statistical bounds of median and standard deviation for two months before and one month after the occurrence of each event. We study the spatial OLR anomaly from National Oceanic and Atmospheric Administration/National Center for Environmental Prediction (NOAA/NCEP). Evidences suggest that abnormal atmospheric anomalies occur within one month before the main shock. For example, in case of Mw 7.2 southwestern Pakistan EQ, significant perturbations in daytime OLR are detected up to 21 days before the main shock, justifying the existence of huge amounts of energy over the tectonic lineaments. The OLR anomaly correlated with ST, AOD and NO2 during 1521 days before the main shock, which may be attributed to the same event. Similarly, the OLR, ST, AOD and NO2 show irregularities before the 2013, Mw 7.7 East of Khash Iran EQ, where all the anomalies occur 910 days before the main shock. The anomalous OLR over the epicenter suggests the authenticity of all the temporal perturbations in the atmospheric parameters related to Mw 7.7 (Iran event). Furthermore, the atmospheric parameters are analyzed temporally by the statistical bounds before the Mw 6.3 (Pasni, Pakistan) EQ. All the parameters behave abnormally during 1015 days following the Mw 6.3 Pakistan EQ and similarly subsequent spatial OLR enhancement over the epicenter may lead to the conclusion of an extensive energy emanation. The anomalies detected are consistent with the processes of stress activation of proxy defects at the Lithosphere-Atmosphere interface in the seismic breeding zone.

Research areas: Earthquakes, Atmospheric/Ocean Indicators
Linkage of aerosol optical depth with rainfall and circulation parameters over the Eastern Gangetic Plains of India (2019)

The aerosol optical depth (AOD) is an important physical parameter and dimensionless number. The possible link between AOD and variability of summer monsoon rainfall and surface temperature over the densely populated Gangetic Plain may be used to assess change in weather and climate over the Plain. For examining the impact of AOD on summer monsoon rainfall and surface temperature, monthly data of AOD for the period of 20002015 are obtained from a remotely sensed moderate resolution imaging spectro-radiometer sensor at 550 nm and at a surface resolution of $$1{^{\circ }}\times 1{^{\circ }}$$11. For the period of 20002015, rainfall and surface temperature data at a resolution of $$1{^{\circ }}\times 1{^{\circ }}$$11are obtained from Indian Meteorological Department (IMD) and surface wind data are obtained from National Centers for Environmental Prediction (NCEP). Summer monsoon rainfall and AOD are inversely related during 20002015. On an average, a difference in the mean monthly surface maximum and minimum temperatures increases (decreases) with a decrease (increase) of AOD. The high degree of correlation exists between AOD and a difference in $$T_{\mathrm{max}}$$Tmaxand $$T_{\mathrm{min}}$$Tminduring January to JuneJuly. In winter months, relative strength of negative vorticity over the Gangetic Plain and positive vorticity in the adjacent area may be cause of more dispersion of AOD in February in comparison with that in December and January and therefore more AOD is noticed in January and December.

Research areas: Validation
Evaluation of evapotranspiration estimates from observed and reanalysis data sets over Indian region (2019)

In this study, we have computed the evapotranspiration (ET) from the input variables of India Meteorological Department (IMD) for different stations in Monsoon Core Region (MCR) of India and Indian Peninsular Region (IPR) and compared with the ERA Interim (ERA-I) and CRU ET data sets. While studying the discrepancies among the data sets, rainfall (source: IMD gridded), relative humidity (source: ERA Interim gridded), air temperature (source: IMD gridded) and soil moisture (source: TRMM/LPRM/TMI-Model) were made use to illustrate the ET variations. When compared with IMD ET, our results show the CRU ET is underestimated but maintained the close pattern over MCR and IPR during South West (SW) monsoon (JuneSeptember) and North East (NE) monsoon (OctoberDecember) period, respectively. ERA-I ET bounded to have mixed response over MCR and are higher than the IMD ET over IPR. Daily comparison of the IMD and ERA-I ET data sets shows a large bias during the beginning of SW monsoon (June month) compared to other months. Site wise correlations show the substantial positive correlations between IMD and CRU ET over MCR than IPR. Overall analysis shows the monsoon features were better explained by the variations in IMD ET compared to CRU and ERA-I ET data sets. The reported disparities among the data sets play an important role in the choice of selection for different applications such as water resource assessments, crop water requirements, monitoring of droughts etc.

Research areas: Validation
Modeling the Sources and Chemistry of Polar Tropospheric Halogens (Cl, Br, and I) Using the CAM-Chem Global Chemistry-Climate Model (2019)

Current chemistry climate models do not include polar emissions and chemistry of halogens. This work presents the first implementation of an interactive polar module into the very short-lived (VSL) halogen version of the Community Atmosphere Model with Chemistry (CAM-Chem) model. The polar module includes photochemical release of molecular bromine, chlorine, and interhalogens from the sea-ice surface, and brine diffusion of iodine biologically produced underneath and within porous sea-ice. It also includes heterogeneous recycling of inorganic halogen reservoirs deposited over fresh sea-ice surfaces and snow-covered regions. The polar emission of chlorine, bromine, and iodine reach approximately 32, 250, and 39 Gg/year for Antarctica and 33, 271, and 4 Gg/year for the Arctic, respectively, with a marked seasonal cycle mainly driven by sunlight and sea-ice coverage. Model results are validated against polar boundary layer measurements of ClO, BrO, and IO, and satellite BrO and IO columns. This validation includes satellite observations of IO over inner Antarctica for which an iodine leapfrog mechanism is proposed to transport active iodine from coastal source regions to the interior of the continent. The modeled chlorine and bromine polar sources represent up to 45% and 80% of the global biogenic VSLCl and VSLBr emissions, respectively, while the Antarctic sea-ice iodine flux is 10 times larger than that from the Southern Ocean. We present the first estimate of the contribution of polar halogen emissions to the global tropospheric halogen budget. CAM-Chem includes now a complete representation of halogen sources and chemistry from pole-to-pole and from the Earth's surface up to the stratopause.

Research areas: Air Quality
Land-sea contrasts for climatic lightning activity over Indian region (2019)

The land-ocean convective contrasts observed by the satellite-driven data from the Tropical Rainfall Measuring Mission for the year 1998 to 2014 (17 years) are analysed to examine the role of static land surface relative to the dynamic ocean surface for the lightning flash counts (FC). We present the relationship between the lightning flash counts per convective available potential energy (CAPE) (FC/CAPE), the product of CAPE and rainfall (CAPE  RF) and Bowen ratio, FC (land/ocean) and AOD (land/ocean) and FC (land/ocean) and maximum updraft speed (land/ocean) to examine the land-ocean contrasts over the Indian region. The results show that FC/CAPE over Indian land increases by up to 520% with respect to FC/CAPE over Indian oceanic regions. The land-ocean contrast seems to be a result of increase of Bowen ratio and FC over land by up to 800% as compared with Bowen ratio and FC over ocean surface. The increase of CAPE over land is by up to 47% relative to CAPE over ocean with corresponding increase of maximum updraft speed over land by up to 117% with respect to maximum updraft speed over ocean. The temperature profiles over the land and ocean show the same temperature in the lower troposphere, followed by a temperature difference of 8 C between land and ocean at about 16000 m during pre-monsoon and post-monsoon periods. The product of CAPE with RF over land is higher by 66% to that of oceanic region with aerosol load (AOD) of about 49% more over land as compared with that of ocean. The results demonstrate the warming contrasts of land-ocean in a comparative analysis of FC, CAPE, RF and AOD in the tropics.

Research areas: Atmospheric/Ocean Indicators