Ahmad, I., Singh, A., Fahad, M., & Waqas, M. M. (2020). Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery. Computers and Electronics in Agriculture, 178.
Abstract: Predicting crop yields and their spatio-temporal variability under a changing climate is a challenging but essential undertaking for crop management and policymaking purposes. The availability of information on risks associated with effects of climatic variability on agricultural activity outcomes is critical for stakeholders ranging from individual landowners to national economists alike. This research was conducted as a pilot study to (1) develop satellite remote sensing based estimates of maize acreage in a typical Maize growing region in Pakistan, (2) to develop a statistical-empirical model for prediction of maize yields, and finally, (3) to assess the influence of temperature on inter-annual variability in maize yields across a decade. A total of eight machine learning algorithms were tested for identifying maize growing operations in the Faisalabad district of Pakistan using Landsat 8 imagery. Classification models were evaluated via 200 randomly selected ground-verified points across the study region. Results of the maize mapping exercise were used to estimate interannual maize yields using Landsat-derived multi-temporal normalized difference vegetation index (NDVI) and land surface temperature (LST) data as predictors. Predictors for the yield forecasting model were selected via principal component screening and were fed into a least absolute shrinkage and selection (LASSO) regression model. The yield model thus developed was applied to 10 years of past data (2006-2017) and validated against data recorded by government sources. Finally, predictions spanning the ten years were tested for effects of temperature variability to find evidence of influence of ambient temperature on maize yields. Results indicate that support vector machine classifiers work the best in this landscape (accuracies >90%) and reveal that maize cropping area may be underestimated in government sources by as much as 14%. The LASSO regression models also showed very good fits (validation R2 = 0.95) and were fairly accurate in tracking interannual variations in maize yields (R2 = 0.78.) Results also indicate that the maximum temperature has significant negative influence (R2 = 0.76, P < 0.0001) on maize yields in Faisalabad district. Methods presented in this study should be of use to policymakers for better formulating export-import policies and decisions governing food security issues in the larger region.
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Bjorndal, K. A., Bolten, A. B., Chaloupka, M., Saba, V. S., Bellini, C., Marcovaldi, M. A. G., et al. (2017). Ecological regime shift drives declining growth rates of sea turtles throughout the West Atlantic. Glob Change Biol, 23(11), 4556–4568.
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Bjorndal, K. A., Chaloupka, M., Saba, V. S., Diez, C. E., van Dam, R. P., Krueger, B. H., et al. (2016). Somatic growth dynamics of West Atlantic hawksbill sea turtles: a spatio-temporal perspective. Ecosphere, 7(5), e01279.
Abstract: Somatic growth dynamics are an integrated response to environmental conditions. Hawksbill sea turtles (Eretmochelys imbricata) are long-lived, major consumers in coral reef habitats that move over broad geographic areas (hundreds to thousands of kilometers). We evaluated spatio-temporal effects on hawksbill growth dynamics over a 33-yr period and 24 study sites throughout the West Atlantic and explored relationships between growth dynamics and climate indices. We compiled the largest ever data set on somatic growth rates for hawksbills -3541 growth increments from 1980 to 2013. Using generalized additive mixed model analyses, we evaluated 10 covariates, including spatial and temporal variation, that could affect growth rates. Growth rates throughout the region responded similarly over space and time. The lack of a spatial effect or spatio-temporal interaction and the very strong temporal effect reveal that growth rates in West Atlantic hawksbills are likely driven by region-wide forces. Between 1997 and 2013, mean growth rates declined significantly and steadily by 18%. Regional climate indices have significant relationships with annual growth rates with 0- or 1-yr lags: positive with the Multivariate El Nino Southern Oscillation Index (correlation = 0.99) and negative with Caribbean sea surface temperature (correlation = -0.85). Declines in growth rates between 1997 and 2013 throughout the West Atlantic most likely resulted from warming waters through indirect negative effects on foraging resources of hawksbills. These climatic influences are complex. With increasing temperatures, trajectories of decline of coral cover and availability in reef habitats of major prey species of hawksbills are not parallel. Knowledge of how choice of foraging habitats, prey selection, and prey abundance are affected by warming water temperatures is needed to understand how climate change will affect productivity of consumers that live in association with coral reefs.
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Chang, N. - B., Imen, S., Bai, K., & Jeffrey Yang, Y. (2017). The impact of global unknown teleconnection patterns on terrestrial precipitation across North and Central America. Atmospheric Research, 193, 107–124.
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Chang, N. - B., Yang, Y. J., Imen, S., & Mullon, L. (2017). Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models. Journal of Hydrology, 548, 305–321.
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Chang, N. B., Imen, S., & Bai, K. (2015). Impacts of Global Non-leading Teleconnection Signals on Terrestrial Precipitation across the United States. In Proceedings of SPIE (Vol. 9610).
Abstract: Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. This study develops a method to overcome the problem of non-stationarity and nonlinearity and investigates how the non-leading teleconnection signals as well as the known teleconnection patterns can affect precipitation over three pristine sites in the United States. It is presented here that the oceanic indices which affect precipitation of specific site do not have commonality in different seasons. Results also found cases in which precipitation is significantly affected by the oceanic regions of two oceans within the same season. We attribute these cases to the combined physical oceanic-atmospheric processes caused by the coupled effects of oceanic regions. Interestingly, in some seasons, different regions in the South Pacific and Atlantic Oceans show more salient effects on precipitation compared to the known teleconnection patterns. Results highlight the importance of considering the seasonality scale and non-leading teleconnection signals in climate prediction.
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Cohuo, S., Macario-González, L., Wagner, S., Naumann, K., Echeverría-Galindo, P., Pérez, L., et al. (2020). Influence of late Quaternary climate on the biogeography of Neotropical aquatic species as reflected by non-marine ostracodes. Biogeosciences, 17(1), 145–161.
Abstract: We evaluated how ranges of four endemic and non-endemic aquatic ostracode species changed in response to long-term (glacial-interglacial cycles) and abrupt climate fluctuations during the last 155 kyr in the northern Neotropical region. We employed two complementary approaches, fossil records and species distribution models (SDMs). Fossil assemblages were obtained from sediment cores PI-1, PI-2, PI-6 and Peten-Itza 22-VIII-99 from the Peten Itza Scientific Drilling Project, Lake Peten Itza, Guatemala. To obtain a spatially resolved pattern of (past) species distribution, a down-scaling cascade is employed. SDMs were reconstructed for the last interglacial (similar to 120 ka), the last glacial maximum (similar to 22 ka) and the middle Holocene (similar to 6 ka). During glacial and interglacial cycles and marine isotope stages (MISs), modelled paleo-distributions and paleo-records show the nearly continuous presence of endemic and non-endemic species in the region, suggesting negligible effects of long-term climate variations on aquatic niche stability. During periods of abrupt ecological disruption such as Heinrich Stadial 1 (HS1), endemic species were resilient, remaining within their current areas of distribution. Non-endemic species, however, proved to be more sensitive. Modelled paleo-distributions suggest that the geographic range of non-endemic species changed, moving southward into Central America. Due to the uncer-tainties involved in the downscaling from the global numerical to the highly resolved regional geospatial statistical modelling, results can be seen as a benchmark for future studies using similar approaches. Given relatively moderate temperature decreases in Lake Peten Itza waters (similar to 5 degrees C) and the persistence of some aquatic ecosystems even during periods of severe drying in HS1, our data suggest (1) the existence of micro-refugia and/or (2) continuous interaction between central metapopulations and surrounding populations, enabling aquatic taxa to survive climate fluctuations in the northern Neotropical region.
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Cruz, R. E. A., Kaplan, D. A., Santos, P. B., Ávila-da-Silva, A. O., Marques, E. E., & Isaac, V. J. (2020). Trends and environmental drivers of giant catfish catch in the lower Amazon River. Mar. Freshwater Res., .
Abstract: The giant catfishes Brachyplatystoma rousseauxii, Brachyplatystoma vaillantii and Brachyplatystoma filamentosum are important environmental, social and economic resources in the Amazon. However, anthropogenic environmental changes, such as climate change, deforestation, overexploitation of water resources and damming of rivers, threaten the conservation of this fishery. The aims of this study were to investigate temporal trends and elucidate global and regional environmental drivers of catch for these species of giant catfish in the Amazon. Using annualised catch data (1993�2010), we tested for linear trends using Mann�Kendall tests and built multilinear models of fish catch using effort and a variety of regional and global hydrological and meteorological series. We found a significant decline in the catches of B. rousseauxii and B. filamentosum, whereas the B. vaillantii catch increased. Total catch had a significant positive correlation with fishing effort, and variation in sea surface temperature (SST) explained an additional 19�38% of the variability of catches. Other hydrological and climate variables were weakly correlated or uncorrelated with catch. Overall, these results argue strongly for a resumption the collection of fishing statistics in the Amazon. In addition, associations between SST and catch suggest that conservation of these long-distance migrants must consider both regional and global drivers of fisheries change.
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Ditri, A., Minnett, P., Liu, Y., Kilpatrick, K., & Kumar, A. (2018). The Accuracies of Himawari-8 and MTSAT-2 Sea-Surface Temperatures in the Tropical Western Pacific Ocean. Remote Sensing, 10(2), 212.
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Evans, D., Sagoo, N., Renema, W., Cotton, L. J., Müller, W., Todd, J. A., et al. (2018). Eocene greenhouse climate revealed by coupled clumped isotope-Mg/Ca thermometry. Proc Natl Acad Sci USA, 115(6), 1174–1179.
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Fraza, E., & Elsner, J. B. (2015). A climatological study of the effect of sea-surface temperature on North Atlantic hurricane intensification. Physical Geography, 36(5), 395–407.
Abstract: The climatic influence of sea-surface temperature (SST) on intensification is examined for North Atlantic hurricanes by averaging hourly intensity increases from best-track data over the period 1986-2013 in 4 degrees by 4 degrees latitude-longitude grid cells. Independent monthly SST data over the same period are averaged in the same cells. After removing cells with cold water or fast moving hurricanes, the SST effect on intensification, at the climate scale, is quantified by regressing intensification onto SST while controlling for average intensity. The regression is performed using a generalized linear model from a gamma family and a logarithmic link function. The model shows a statistically significant relationship, with higher intensification values associated with higher SST values. On average, mean intensification increases by 16% [(9,20)% uncertainty interval] for every 1 degrees C increase in mean SST. A clustered region where the model underpredicts intensification is noted over the southeastern Caribbean Sea, perhaps related to the fresh water plume from the Orinoco River.
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Freeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E., et al. (2017). ICOADS Release 3.0: a major update to the historical marine climate record. Int. J. Climatol., 37(5), 2211–2232.
Abstract: We highlight improvements to the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) in the latest Release 3.0 (R3.0; covering 1662�2014). ICOADS is the most widely used freely available collection of surface marine observations, providing data for the construction of gridded analyses of sea surface temperature, estimates of air�sea interaction and other meteorological variables. ICOADS observations are assimilated into all major atmospheric, oceanic and coupled reanalyses, further widening its impact. R3.0 therefore includes changes designed to enable effective exchange of information describing data quality between ICOADS, reanalysis centres, data set developers, scientists and the public. These user-driven innovations include the assignment of a unique identifier (UID) to each marine report � to enable tracing of observations, linking with reports and improved data sharing. Other revisions and extensions of the ICOADS' International Maritime Meteorological Archive common data format incorporate new near-surface oceanographic data elements and cloud parameters. Many new input data sources have been assembled, and updates and improvements to existing data sources, or removal of erroneous data, made. Coupled with enhanced �preliminary� monthly data and product extensions past 2014, R3.0 provides improved support of climate assessment and monitoring, reanalyses and near-real-time applications.
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Gilford, D. M., Smith, S. R., Griffin, M. L., & Arguez, A. (2013). Southeastern U.S. Daily Temperature Ranges Associated with the El Nino-Southern Oscillation. J. Appl. Meteor. Climatol., 52(11), 2434–2449.
Abstract: The daily temperature range (DTR; daily maximum temperature minus daily minimum temperature) at 290 southeastern U.S. stations is examined with respect to the warm and cold phases of the El Niño�Southern Oscillation (ENSO) for the period of 1948�2009. A comparison of El Niño and La Niña DTR distributions during 3-month seasons is conducted using various metrics. Histograms show each station�s particular distribution. To compare directly the normalized distributions of El Niño and La Niña, a new metric (herein called conditional ratio) is produced and results are evaluated for significance at 95% confidence with a bootstrapping technique. Results show that during 3-month winter, spring, and autumn seasons DTRs above 29°F (16.1°C) are significantly more frequent during La Niña events and that DTRs below 15°F (8.3°C) are significantly more frequent during El Niño events. It is hypothesized that these results are associated spatially with cloud cover and storm tracks during each season and ENSO phase. Relationships between DTRs and ENSO-related relative humidity are examined. These results are pertinent to the cattle industry in the Southeast, allowing ranchers to plan for and mitigate threats posed by periods of low DTRs associated with the predicted phase of ENSO.
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Hodges, R. E., Jagger, T. H., & Elsner, J. B. (2014). The sun-hurricane connection: Diagnosing the solar impacts on hurricane frequency over the North Atlantic basin using a space-time model. Nat Hazards, 73(2), 1063–1084.
Abstract: The authors define a spatio-statistical response of hurricane frequency to the solar cycle. Previous research indicates reduced (increased) hurricane intensities and frequency in the western (eastern) tropical Atlantic. However, no formal quantitative relationship has been spatially established between hurricane frequency and solar activity. The authors use a Bayesian hierarchical space–time model, an increasingly popular approach due to its advantage in facilitating regression modeling of space–time phenomena in the context of large data sets. Regional hurricane frequency over the period 1866–2010 is examined in response to September sunspot number (SSN) while controlling for other relevant climate factors. The response features a 13 % reduction in probability of annual hurricane occurrence for southeastern Cuba, the southern Bahama islands, Haiti, and Jamaica when the SSN is 80 sunspots. In contrast, hurricane risk in regions of the southeastern Atlantic is predicted to increase by 73 % when the SSN is 160 sunspots. The model can be ported to explore other relationships over contiguous space.
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Hua, W., Zhou, L., Chen, H., Nicholson, S. E., Jiang, Y., & Raghavendra, A. (2018). Understanding the Central Equatorial African long-term drought using AMIP-type simulations. Clim Dyn, 50(3-4), 1115–1128.
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Hua, W., Zhou, L., Chen, H., Nicholson, S. E., Raghavendra, A., & Jiang, Y. (2016). Possible causes of the Central Equatorial African long-term drought. Environ. Res. Lett., 11(12), 124002.
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Katsaros, K. B., A. Bentamy, M. Bourassa, N. Ebuchi, J. Gower, W. T. Liu, and S. Vignudelli. (2011). Climate Data Issues from an Oceanographic Remote Sensing Perspective. In Remote Sensing of the Changing Oceans (pp. 7–32). Berlin, Germany: Springer-Verlag.
Abstract: In this chapter we review several climatologically important variables
with a history of observation from spaceborne platforms. These include sea surface
temperature and wind vectors, altimetric estimates of sea surface height, energy and
water vapor fluxes at the sea surface, precipitation over the ocean, and ocean color.
We then discuss possible improvements in sampling for climate and climate change
definition. Issues of consistency of different data sources, archiving and distribution
of these types of data are discussed. The practical prospect of immediate international
coordination through the concept of virtual constellations is discussed and
applauded.
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Laureano-Rosario, A. E., Garcia-Rejon, J. E., Gomez-Carro, S., Farfan-Ale, J. A., & Muller-Karger, F. E. (2017). Modelling dengue fever risk in the State of Yucatan, Mexico using regional-scale satellite-derived sea surface temperature. Acta Tropica, 172, 50–57.
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Lin, H., Fan, Z., Shi, L., Arain, A., McCaughey, H., Billesbach, D., et al. (2017). The Cooling Trend of Canopy Temperature During the Maturation, Succession, and Recovery of Ecosystems. Ecosystems, 20(2), 406–415.
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Liu, Y., & Minnett, P. J. (2015). Evidence linking satellite-derived sea-surface temperature signals to changes in the Atlantic meridional overturning circulation. Remote Sensing of Environment, 169, 150–162.
Abstract: This study explores an application of satellite-derived Sea Surface Temperature (SST) to climate studies by focusing on a connection with the Atlantic Meridional Overturning Circulation (AMOC). Here we focus on SSTs from the advanced very high resolution radiometer and report a 99% significant correlation between the changes of in situ measured AMOC transport and the variation of 1-month leading SST anomalies in the subpolar North Atlantic region (45 degrees N-70 degrees N) based on analyses of an 85-month period. The leading mode of the singular value decomposition analysis of SST and Sea Level Pressure (SLP) for 31 years (1981/12-2012/12) shows an apparent North Atlantic Oscillation (NAO) forcing on the SST fields. Specifically, the 551 and SLP one-month phase lag covariance is notable at temporal scales of 4 to 11 months. After removing the first order component of the NAO, the residual SST (RESST) provides better estimates of the AMOC on a shorter time scale than the SST. This is because that RESST is less likely to be affected by the local SLP on these time scales. The high correlation is primarily between the RESST and variations of the geostrophic Upper Mid-Ocean transport component of the AMOC. The 31-year RESST time series in the North Atlantic subpolar region is also significantly correlated with the Gulf Stream path SST anomalies with a one-month lead, implying a fast signal transport from the subpolar North Atlantic to the Gulf Stream. A similar fast adjustment signal is also found in 500-year control simulations of the GFDL model CM2.1. These results indicate a prospective capability of satellite-derived SSTs to predict AMOC variability.
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