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During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large‐scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño‐southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country‐based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out‐of‐sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

.[1] The components of the Amazon water budget and their spatiotemporal variability are diagnosed using monthly averaged remote sensing-based data products for the period September 2002-December 2006. The large Amazon basin is divided into 14 smaller watersheds, and for each of these sub-basins, fresh water discharge is estimated from the water balance equation using satellite data products. The purpose of this study is to learn how to apply satellite data with global coverage over the large tropical regions; therefore several combinations of remote sensing estimates including total water storage changes, precipitation and evapotranspiration. The results are compared to gauge-based measurements and the best spatiotemporal agreement between estimated and observed runoff is within 1 mm/d for the combination of precipitation from the GPCP and the Montana evapotranspiration product. Mean annual precipitation, evapotranspiration and runoff for the whole basin are estimated to be 6.3, 2.27 and 3.02 mm/d respectively but also show large spatial and temporal variations at sub-basin scale. Using the most consistent data combination, the seasonal dynamics of the water budget within the Amazon system are examined. Agreement between satellite based and in situ runoff is improved when lag-times between sub-basins are included (RMSE from 0.98 to 0.61 mm/d). We estimate these lag times based on satellite-inferred inundation extents. The results reveal not only variations of the basin forcing but also the complex response of the inter-connected sub-basin (SB) water budgets. Inter-annual and inter-SB variation of the water components are investigated and show large anomalies in northwestern and eastern downstream SBs; aggregate behavior of the whole Amazon is more complex than can be represented by a simple integral of the forcing over the whole river system.

Abstract. Hourly Satellite Precipitation Estimates (SPEs) may be the only available source of information for operational hydrologic and flash flood prediction due to spatial limitations of radar and gauge products. SPEs are prone to larger systematic errors and more uncertainty sources in comparison with ground based radar and gauge precipitation products. The present work develops an approach to seamlessly blend satellite, radar and gauge products to fill gaps in ground-based data. To mix different rainfall products, the bias of any of the products relative to each other should be removed. The study presents and tests a proposed ensemblebased method which aims to estimate spatially varying multiplicative biases in hourly SPEs using a radar-gauge rainfall product and compare it with previously used bias correction methods. Bias factors were calculated for a randomly selected sample of rainy pixels in the study area. Spatial fields of estimated bias were generated taking into account spatial variation and random errors in the sampled values. Bias field parameters were determined on a daily basis using the shuffled complex evolution optimization algorithm. To include more error sources, ensembles of bias factors were generated and applied before bias field generation. We demonstrate this method using two satellite-based products, CPC Morphing (CMORPH) and Hydro-Estimator (HE), and a radar-gauge rainfall Stage-IV (ST-IV) dataset for several rain events in 2006 over Oklahoma. The method was compared with 3 simpler methods for bias correction: mean ratio, maximum ratio and spatial interpolation without ensembles. Bias ratio, correlation coefficient, root mean square error and mean absolute difference are used to evaluate the performance of the different methods. Results show that: (a) the methods of maximum ratio and mean ratio performed variably and did not improve the overall correlation with the ST-IV in any of Correspondence to: K. Tesfagiorgis (ktesfagiorgis@gc.cuny.edu) the rainy events; (b) the method of interpolation was consistently able to improve all the performance criteria; (c) the method of ensembles outperformed the other 3 methods.

High-albedo white and cool roofing membranes are recognized as a fundamental strategy that dense urban areas can deploy on a large scale, at low cost, to mitigate the urban heat island effect. We are monitoring three generic white membranes within New York City that represent a cross section of the dominant white membrane options for US flat roofs: (1) an ethylene-propylene-diene monomer (EPDM) rubber membrane; (2) a thermoplastic polyolefin (TPO) membrane; and (3) an asphaltic multi-ply built-up membrane coated with white elastomeric acrylic paint. The paint product is being used by New York City's government for the first major urban albedo enhancement program in its history. We report on the temperature and related albedo performance of these three membranes at three different sites over a multi-year period. The results indicate that the professionally installed white membranes are maintaining their temperature control effectively and are meeting the Energy Star Cool Roofing performance standards requiring a three-year aged albedo above 0.50. The EPDM membrane shows evidence of low emissivity; however this had the interesting effect of avoiding any 'winter heat penalty' for this building. The painted asphaltic surface shows high emissivity but lost about half of its initial albedo within two years of installation. Given that the acrylic approach is such an important 'do-it-yourself', low-cost, retrofit technique, and, as such, offers the most rapid technique for increasing urban albedo, further product performance research is recommended to identify conditions that optimize its long-term albedo control. Even so, its current multi-year performance still represents a significant albedo enhancement for urban heat island mitigation.

Green roofs (with plant cover) are gaining attention in the United States as a versatile new environmental mitigation technology. Interest in data on the environmental performance of these systems is growing, particularly with respect to urban heat island mitigation and stormwater runoff control. We are deploying research stations on a diverse array of green roofs within the New York City area, affording a new opportunity to monitor urban environmental conditions at small scales. We show some green roof systems being monitored, describe the sensor selection employed to study energy balance, and show samples of selected data. These roofs should be superior to other urban rooftops as sites for meteorological stations.

This study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions.

Reza, "Using microwave brightness temperature diurnal cycle to improve emissivity retrievals over land" (2012 To retrieve microwave land emissivity, infrared surface skin temperatures have been used as surface physical temperature since there is no global information on physical vegetation/soil temperature profiles. However, passive microwave emissions originate from deeper layers with respect to the skin temperature. So, this inconsistency in sensitivity depths between skin temperatures and microwave temperatures may introduce a discrepancy in the determined emissivity. Previous studies showed that this inconsistency can lead to significant differences between day and night retrievals of land emissivity which can exceed 10%. This study proposes an approach to address this inconsistency and improve the retrieval of land emissivity using microwave observations from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The diurnal cycle of the microwave brightness temperature (Tb) was constructed over the globe for different frequencies/polarizations using a constellation of satellites. Principal component analysis (PCA) was conducted to evaluate the spatial variation of the Tb diurnal cycle. The diurnal amplitudes of microwave temperatures observed in desert areas were not consistent with the larger amplitudes of the diurnal cycle of skin temperature. Densely vegetated areas with more moisture have shown smaller amplitudes. A lookup table of effective temperature (T eff ) anomalies is constructed based on the Tb diurnal cycle to resolve the inconsistencies between infrared and Tb diurnal variation. This lookup table of T eff anomalies is a weighted average over the layers contributing to the microwave signal, for each channel and month. The integration of this T eff in the retrieval of land emissivity reduced the differences between day and night retrieved emissivities to less than 0.01 for AMSR-E observations.