Chaoshun Liu

Determining climate effects on US total agricultural productivity

Significance Projections of the economic consequences of climate change are valuable for policy making but generally rely on integrated assessments that cannot account for highly localized climate effects. Most agricultural climate impact studies focus on local effects or partial productivity measures insufficient to capture national economic outcomes. Here, we directly link climate variables in specific US regions to total factor productivity (TFP). We quantify the national economic consequences of past climate variations, identify critical agricultural regions with national significance, and project future changes in TFP under different climate scenarios. We provide a physical understanding of these climate−economic links, show that the agricultural economy is becoming increasingly sensitive to climate, and lay a more concrete foundation for informed decision-making. The sensitivity of agricultural productivity to climate has not been sufficiently quantified. The total factor productivity (TFP) of the US agricultural economy has grown continuously for over half a century, with most of the growth typically attributed to technical change. Many studies have examined the effects of local climate on partial productivity measures such as crop yields and economic returns, but these measures cannot account for national-level impacts. Quantifying the relationships between TFP and climate is critical to understanding whether current US agricultural productivity growth will continue into the future. We analyze correlations between regional climate variations and national TFP changes, identify key climate indices, and build a multivariate regression model predicting the growth of agricultural TFP based on a physical understanding of its historical relationship with climate. We show that temperature and precipitation in distinct agricultural regions and seasons explain ∼70% of variations in TFP growth during 1981–2010. To date, the aggregate effects of these regional climate trends on TFP have been outweighed by improvements in technology. Should these relationships continue, however, the projected climate changes could cause TFP to drop by an average 2.84 to 4.34% per year under medium to high emissions scenarios. As a result, TFP could fall to pre-1980 levels by 2050 even when accounting for present rates of innovation. Our analysis provides an empirical foundation for integrated assessment by linking regional climate effects to national economic outcomes, offering a more objective resource for policy making.

Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China

The Normalized Difference Vegetation Index (NDVI) is an important vegetation greenness indicator. Compared to the AVHRR GIMMS NDVI data, the availability of two datasets with 1 km spatial resolution, i.e., Terra MODIS (MOD13A3) monthly composite and SPOT Vegetation (VGT) 10-day composite NDVI, extends the application dimensions at spatial and temporal scales. An overlapping period of 12 years between the datasets now makes it possible to investigate the consistency of the two datasets. Linear regression trend analysis was performed to compare the two datasets in this study. The results show greater consistency in regression slopes in the semi-arid regions of northern China. Alternatively, the results show only slight changes in the Terra MODIS NDVI regression slope in most areas of southern China whereas the SPOT VGT NDVI shows positive changes over a large area. The corresponding regression slope values between Terra MODIS and SPOT VGT NDVI datasets from the linear fit had a fair agreement in the spatial dimension. However, larger positive and negative differences were observed at the junction of the three regions (East China, Central China, and North China). These differences can be partially explained by the positive standard deviation differences distributed over a large area at the junction of these three regions. This study demonstrated that Terra MODIS and SPOT VGT NDVI have a relatively robust basis for characterizing vegetation changes in annual NDVI in most of the semi-arid and arid regions in northern China.

Global validation of FY-3A total ozone unit TOU total ozone columns using ground-based Brewer and Dobson measurements

Analysis of the accuracy and variability of total ozone columns (TOC) has been conducted by many studies, while the TOC observations derived from the total ozone unit (TOU) on board the Chinese FengYun-3A (FY-3A) satellite platform are notably less well documented. Therefore, in this present study, we mainly focus on the global-scale validation of TOU-derived total ozone column data by comparing them with spatially and temporally co-located ground-based measurements from the well-established Brewer and Dobson spectrophotometer for the period July 2009 through December 2011. The results show that TOU-derived total ozone column data yields high accuracy, with the root mean square error less than 5% in comparison with ground-based measurements. Meanwhile, TOU underestimates Brewer measurements by 1.1% in the Northern Hemisphere and overestimates Dobson total ozone 0.3% globally. In addition, TOU-derived total ozone shows no significant dependence on latitude in comparison with either Brewer or Dobson total ozone measurements. Nevertheless, a significant dependence of TOU-derived total ozone is observed on the solar zenith angle (SZA) in comparison with both Brewer and Dobson, demonstrating that TOU underestimates at large SZA and overestimates at small SZA. Finally, the dependence of satellite – ground-based relative difference for total ozone values shows fair agreement when total ozone values are in the range 250–450 Dobson units (DU). Overall, the Chinese FY-3A/TOU performs well on total ozone retrieval with high accuracy, and the total ozone data derived from the TOU can be used as a reliable data source for ozone monitoring and other atmospheric applications.

Analysis of air quality variability in Shanghai using AOD and API data in the recent decade

We use the aerosol optical depth (AOD) measured by the moderate resolution imaging spectrometer (MODIS) onboard the Terra satellite, air pollution index (API) daily data measured by the Shanghai Environmental Monitoring Center (SEMC), and the ensemble empirical mode decomposition (EEMD) method to analyze the air quality variability in Shanghai in the recent decade. The results indicate that a trend with amplitude of 1.0 is a dominant component for the AOD variability in the recent decade. During the World Expo 2010, the average AOD level reduced 30% in comparison to the long-term trend. Two dominant annual components decreased 80% and 100%. This implies that the air quality in Shanghai was remarkably improved, and environmental initiatives and comprehensive actions for reducing air pollution are effective. AOD and API variability analysis results indicate that semi-annual and annual signals are dominant components implying that the monsoon weather is a dominant factor in modulating the AOD and API variability. The variability of AOD and API in selected districts located in both downtown and suburban areas shows similar trends; i.e., in 2000 the AOD began a monotonic increase, reached the maxima around 2006, then monotonically decreased to 2011 and from around 2006 the API started to decrease till 2011. This indicates that the air quality in the entire Shanghai area, whether urban or suburban areas, has remarkably been improved. The AOD improved degrees (IDS) in all the selected districts are (8.6±1.9)%, and API IDS are (9.2±7.1)%, ranging from a minimum value of 1.5% for Putuo District to a maximum value of 22% for Xuhui District.

A correlation analysis of monthly mean CO2 retrieved from the Atmospheric Infrared Sounder with surface station measurements

As one of the major greenhouse gases, atmospheric carbon dioxide (CO2) concentrations have been monitored by both top-down satellite observations and air sampling systems on surface stations. The Atmospheric Infrared Sounder (AIRS) on board NASA’s Aqua low Earth orbit (LEO) satellite is a high-resolution infrared sounder that has been in operation for more than 10 years. The World Data Centre for Greenhouse Gases (WDCGG) archives and provides data on CO2 and other greenhouse gases measured mainly from surface stations. In this article, we focus on the correlation between the two different sources of CO2 data and the influencing factors. In general, we find that a linear positive correlation occurs at most stations. However, the variation in the correlation coefficient is large, especially for stations in the Northern Hemisphere. The station’s location, including its latitude, longitude, and altitude, is an important influencing factor because it determines how much its CO2 measurements are influenced by human activities. We also use root mean square difference (RMSD) and bias as evaluation indicators and find that they have similar trends like correlation coefficients.

Retrieval of columnar water vapor using multispectral radiometer measurements over northern China

Water vapor is an important component in hydrological processes that basically involve all types of seasons, including dry (e.g., drought) or wet (e.g., hurricane or monsoon). This study retrieved columnar water vapor (CWV) with the 939.3 nm band of a multifilter rotating shadowband radiometer (MFRSR) using the modified Langley technique. Such an investigation was in concert with the use of the atmospheric transmission model MODTRAN for determining the instrument coefficients required for CWV estimation. Results of the retrieval of CWV by MFRSR from September 23, 2004 to June 20, 2005 at the XiangHe site are presented and analyzed in this paper. To improve the credibility, the MFRSR results were compared with those obtained from the AErosol RObotic NETwork CIMEL sun-photometer measurements, co-located at the XiangHe site, and the moderate resolution imaging spectroradiometer (MODIS) near-infrared total precipitable water product (MOD05), respectively. These comparisons show good agreement in terms of correlation coefficients, slopes, and offsets, revealing that the accuracy of CWV estimation using the MFRSR instrument is reliable and suitable for extended studies in northern China.

Comparison of Suomi-NPP OMPS total column ozone with Brewer and Dobson spectrophotometers measurements

The objective of this study is to evaluate the accuracy of the daily nadir total column ozone products derived from the nadir mapper instrument on the Ozone Mapping and Profiler Suite (OMPS) flying onboard the Suomi National Polar-orbiting Partnership satellite (S-NPP) launched as a part of the Joint Polar Satellite System (JPSS) program between NOAA and NASA. Since NOAA is already operationally processing OMPS nadir total ozone products, evaluations were made in this study on the total column ozone research products generated by NASA’s science team, utilizing the latest version of their Backscatter Ultraviolet (BUV) retrieval algorithms, to provide insight into the performance of the operation system. Comparisons were made with globally distributed ground-based Brewer and Dobson spectrophotometer total column ozone measurements. Linear regressions show fair agreement between OMPS and ground-based total column ozone measurements with a root-mean-square error (RMSE) of approximately 3% (10 DU). The comparison results indicate that the OMPS total column ozone data are 0.59% higher than the Brewer measurements with a standard deviation of 2.82% while 1.09% higher than the Dobson measurements with a standard deviation of 3.27%. Additionally, the variability of relative differences between OMPS and ground total column ozone were analyzed as a function of latitude, time, viewing geometry, and total column ozone value. Results show a 2% bias over most latitudes and viewing conditions when total column ozone value varies between 220 DU and 450 DU.

Evaluation of CALIPSO aerosol optical depth using AERONET and MODIS data over China

Aerosol optical depth (AOD) data from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were inter-compared and validated against ground-based measurements from Aerosol Robotic Network (AERONET) as well as Moderate Resolution Imaging Spectroradiometer (MODIS) over China during June 2006 to December 2012. We have compared the AOD between CALIOP and AERONET site by site using quality control flags to screen the AOD data. In general, CALIOP AOD is lower than AERONET due to cloud effect detected algorithm and retrieval uncertanty. Better agreement is apparent for these sites: XiangHe, Beijing, Xinglong, and SACOL. Low correlations were observed between CALIPSO and ground-based sunphotometer data in in south or east China. Comparison results show that the overall spatio-temporal distribution of CALIPSO AOD and MODIS AOD are basically consistent. As for the spatial distribution, both of the data show several high-value regions and low-value regions in China. CALIPSO is systematically lower than MODIS over China, especially over high AOD value regions for all seasons. As for the temporal variation, both data show a significant seasonal variation: AOD is largest in spring, then less in summer, and smallest in winter and autumn. Statistical frequency analysis show that CALIPSO AOD and MODIS AOD was separated at the cut-off points around 0.2 and 0.8, the frequency distribution curves were almost the same with AOD between 0.2 and 0.8, while AOD was smaller than 0.4, CALIPSO AOD gathered at the low-value region (0-0.2) and the frequency of MODIS AOD was higher than CALIPSO AOD with AOD greater than 0.8. CALIOP AOD values show good correlation with MODIS AOD for all time scales, particularly for yearly AOD with higher correlation coefficient of 0.691. Seasonal scatterplot comparisons suggest the highest correlation coefficient of 0.749 in autumn, followed by winter of 0.665, summer of 0.566, and spring of 0.442. Evaluation of CALIOP AOD retrievals provides prospect application for CALIPSO data.

Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery

Accurate mapping of invasive species in a cost-effective way is the first step toward understanding and predicting the impact of their invasions. However, it is challenging in coastal wetlands due to confounding effects of biodiversity and tidal effects on spectral reflectance. The aim of this work is to describe a method to improve the accuracy of mapping an invasive plant (Spartina alterniflora), which is based on integration of pan-sharpening and classifier ensemble techniques. A framework was designed to achieve this goal. Five candidate image fusion algorithms, including principal component analysis fusion algorithm, modified intensity-huesaturation fusion algorithm, wavelet-transform fusion algorithm, Ehlers fusion algorithm, and Gram-Schmidt fusion algorithm, were applied to pan-sharpening Landsat 8 operational land imager (OLI) imagery. We assessed the five fusion algorithms with respect to spectral and spatial fidelity using visual inspection and quantitative quality indicators. The optimal fused image was selected for subsequent analysis. Then, three classifiers, namely, maximum likelihood, artificial neural network, and support vector machine, were employed to preclassify the fused and raw OLI 30-m band images. Final object-based S. alterniflora maps were generated through classifier ensemble analysis of outcomes from the three classifiers. The results showed that the introduced method obtained high classification accuracy, with an overall accuracy of 90.96% and balanced misclassification errors between S. alterniflora and its coexistent species. We recommend future research to adopt the proposed method for monitoring long-term or multiseasonal changes in land coverage of invasive wetland plants

Tuning of background error statistics through sensitivity experiments and its impact on typhoon forecast

Abstract. Background error covariance (B) matrix is critical for variational data assimilation as it greatly affects the analyses of three-dimensional variational assimilation. The National Meteorological Center method was used to estimate the B matrix using the forecasts from the Advanced Research Weather Research and Forecasting regional model. To further understand and evaluate the newly generated regional B matrix, its characteristics were compared with the global B estimated from the Global Forecast System model. Sensitivity experiments were undertaken by changing the horizontal length-scales and standard deviations of the B matrix, and its impacts on the typhoon forecast were also examined. Verification against radiosonde observations showed that the varying horizontal length-scale has a significant positive impact on the 24-h forecast of temperature, specific humidity, u-wind, and v-wind. On the other hand, changing standard deviations of the B matrix has a slight influence only on the specific humidity and wind (u-component) forecast. Compared with the global B, the tuned regional B showed improvements in temperature forecasts. In addition, using the tuned regional B also led to a positive impact on the typhoon (Saola, Damrey, and Haikui) track forecasts as compared with the untuned B and global B.