No Consistent Evidence for Advancing or Delaying Trends in Spring Phenology on the Tibetan Plateau

Vegetation phenology is a sensitive indicator of climate change and has significant effects on the exchange of carbon, water, and energy between the terrestrial biosphere and the atmosphere. The Tibetan Plateau, the Earth\u27s “third pole,” is a unique region for studying the long‐term trends in vegetation phenology in response to climate change because of the sensitivity of its alpine ecosystems to climate and its low‐level human disturbance. There has been a debate whether the trends in spring phenology over the Tibetan Plateau have been continuously advancing over the last two to three decades. In this study, we examine the trends in the start of growing season (SOS) for alpine meadow and steppe using the Global Inventory Modeling and Mapping Studies (GIMMS)3g normalized difference vegetation index (NDVI) data set (1982–2014), the GIMMS NDVI data set (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data set (2001–2014), the Satellite Pour l\u27Observation de la Terre Vegetation (SPOT‐VEG) NDVI data set (1999–2013), and the Sea‐viewing Wide Field‐of‐View Sensor (SeaWiFS) NDVI data set (1998–2007). Both logistic and polynomial fitting methods are used to retrieve the SOS dates from the NDVI data sets. Our results show that the trends in spring phenology over the Tibetan Plateau depend on both the NDVI data set used and the method for retrieving the SOS date. There are large discrepancies in the SOS trends among the different NDVI data sets and between the two different retrieval methods. There is no consistent evidence that spring phenology (“green‐up” dates) has been advancing or delaying over the Tibetan Plateau during the last two to three decades. Ground‐based budburst data also indicate no consistent trends in spring phenology. The responses of SOS to environmental factors (air temperature, precipitation, soil temperature, and snow depth) also vary among NDVI data sets and phenology retrieval methods. The increases in winter and spring temperature had offsetting effects on spring phenology

Remote sensing of glacier change in the Central Qinghai-Tibet Plateau and the relationship with changing climate

2016-2017 > Academic research: refereed > Publication in refereed journal201804_a bcmaVersion of RecordPublishe

PICES Press, Vol. 5, No. 2, July 1997

WG 10 Fukuoka Workshop Pacific salmon:climate-linked long-term stock fluctuations The state of the eastern North Pacific in the second half of 1996 The state of the western North Pacific in the second half of 1996 The status of the Bering Sea in the second half of 1996 Yutaka Nagata Eulogy A brief look at mechanisms for support of oceanographic research in the United States Research interests and the funding system for the new Ministry of Maritime Affairs and Fisheries of the Republic of Korea PICES and electronic communication Japan Meteorological Agency: oceanographic activitie

Air Quality Research Using Remote Sensing

Air pollution is a worldwide environmental hazard that poses serious consequences not only for human health and the climate but also for agriculture, ecosystems, and cultural heritage, among other factors. According to the WHO, there are 8 million premature deaths every year as a result of exposure to ambient air pollution. In addition, more than 90% of the world’s population live in areas where the air quality is poor, exceeding the recommended limits. On the other hand, air pollution and the climate co-influence one another through complex physicochemical interactions in the atmosphere that alter the Earth’s energy balance and have implications for climate change and the air quality. It is important to measure specific atmospheric parameters and pollutant compound concentrations, monitor their variations, and analyze different scenarios with the aim of assessing the air pollution levels and developing early warning and forecast systems as a means of improving the air quality and safeguarding public health. Such measures can also form part of efforts to achieve a reduction in the number of air pollution casualties and mitigate climate change phenomena. This book contains contributions focusing on remote sensing techniques for evaluating air quality, including the use of in situ data, modeling approaches, and the synthesis of different instrumentations and techniques. The papers published in this book highlight the importance and relevance of air quality studies and the potential of remote sensing, particularly that conducted from Earth observation platforms, to shed light on this topic

Global vegetation variability and its response to elevated CO2, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data

Abstract. The climate regime shift during the 1980s had a substantial impact on the terrestrial ecosystems and vegetation at different scales. However, the mechanisms driving vegetation changes, before and after the shift, remain unclear. In this study, we used a biophysical-dynamic vegetation model to estimate large-scale trends in terms of carbon fixation, vegetation growth, and expansion during the period 1958–2007, and to attribute these changes to environmental drivers including elevated atmospheric CO2 concentration (hereafter eCO2), global warming, and climate variability (hereafter CV). Simulated Leaf Area Index (LAI) and Gross Primary Product (GPP) were evaluated against observation-based data. Significant spatial correlations are found (correlations > 0.87), along with regionally varying temporal correlations of 0.34–0.80 for LAI and 0.45–0.83 for GPP. More than 40 % of the global land area shows significant trends in LAI and GPP since the 1950s: 11.7 % and 19.3 % of land has consistently positive LAI and GPP trends, respectively; while 17.1 % and 20.1 % of land, saw LAI and GPP trends respectively, reverse during the 1980s. Vegetation fraction cover (FRAC) trends, representing vegetation expansion/shrinking, are found at the edges of semi-arid areas and polar areas. Overall, eCO2 consistently contributes to positive LAI and GPP trends in the tropics. Global warming is shown to mostly affected LAI, with positive effects in high latitudes and negative effects in subtropical semi-arid areas. CV is found to dominate the variability of FRAC, LAI, and GPP in the semi-humid and semi-arid areas. The eCO2 and global warming effects increased after the 1980s, while the CV effect reversed during the 1980s. In addition, plant competition is shown to have played an important role in determining which driver dominated the regional trends. This paper presents a new insight into ecosystem variability and changes in the varying climate since the 1950s

APPLICATIONS OF MODERATE-RESOLUTION REMOTE SENSING TECHNOLOGIES FOR SURFACE AIR POLLUTION MONITORING IN SOUTHEAST ASIA

Retrievals from Earth observation satellites are widely used for many applications, including analyzing dynamic lands and measuring atmospheric components. This research aims to evaluate appropriateness of using satellite retrievals to facilitate understanding characteristics of Southeast Asian (SEA) surface air pollution, attributed to regional biomass burnings and urban activities. The studies in this dissertation focused on using satellite retrievals for 1) mapping potential SEA air pollution sources; which are forests, rice paddies, and urban areas, 2) understanding dynamic optical characteristics of SEA biomass-burning aerosols, and 3) inferring surface ozone level. Data used in this study were from three NASA\u27s Earth Observing System (EOS) satellites, which are Terra, Aqua, and Aura. These retrievals have spatial resolution ranging from hundred meters to ten kilometers. Algorithms used for the SEA land cover classification were developed using time-series analyses of surface reflectance in multiple wavelength bands from Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. Comparing the results to national statistical databases, good agreement was obtained for spatial estimation of forest areas after correction with plantation areas. For estimation of rice paddies areas, the agreement depended on the rice ecosystems. It was good for rainfed rice and poor for deepwater rice. Models for irrigated and upland rice areas showed overall high coefficients of determination, suggesting that they effectively simulated the spatial distribution of those rice paddies; but were prone to underestimate and overestimate, respectively. Estimated SEA regional rice area was 42×106 ha, which agrees with previous published values. Analysis of the satellite retrieval could identify large urban areas. However, the satellite-derived urban areas also incorrectly included large sandy beaches. Optical properties of SEA background aerosols were investigated through the multivariate analyses of long-term ground-based aerosol measurements acquired from Aerosol Robotic Network (AERONET). The results in this study showed that from mid-September to December, the aerosol had both fine size and high light scattering efficiency. It was assumed to be largely urban/industrial aerosols, possibly coming from eastern China. From January to April, the aerosol had fine size and had single scattering albedo (SSA at 440 nm) of approximately 0.9. It was assumed to be smoke from local biomass burning. From October to January, when seasonal winds are strongest, more SEA urban aerosol was observed. This aerosol had coarser size and had SSA of ~0.9 or less. The appropriateness of using Ozone Monitoring Instrument (OMI) aerosol retrieval to facilitate understanding SEA biomass-burning aerosol properties was evaluated through three lines of evidence. These are 1) comparisons between the results obtained from multivariate analyses of the OMI aerosol retrieval and those obtained from the ground-measured AERONET data, 2) from Atmospheric Infrared Sounder (AIRS) total column CO product, and 3) from MODIS active fire detections. The results showed that the OMI retrieval used for large-scale SEA biomass-burning aerosol characterization was consistent with these alternative measures only when 1 \u3c OMI aerosol optical depth (442 nm) \u3c 3. The OMI aerosol retrieval was then used for the study on dynamic characteristics of biomass burning aerosol. This study considered the aerosols from two forest-fire episodes, 2007 SEA continent and 2008 Indonesian fires. Dependence of the aerosol optical properties on four variables was investigated. These variables were 1) wind speed/direction, 2) relative humidity (RH), 3) land use/cover as a surrogate of fuel type estimated from time-series analysis of MODIS surface reflectance, and 4) age of aerosol estimated from spatial-temporal analysis of MODIS active fire and the wind characteristics. Results from Pearson Chi-square test for independence showed that the dependence between aerosol group memberships with different optical properties and the limiting variables was significant for most cases, except for Indonesian aerosol age factor. These results agree with prior knowledge on regional burning conditions (types of fuel and relative humidity) and aerosol chemical/physical properties (chemical composition related to aerosol optical properties and hygroscopicity). Using EOS-Aura tropospheric column ozone (TCO) to infer surface ozone level was evaluated through analyses of linear relationships between TCO estimated from OMI and Microwave Limb Sounder (MLS) retrievals and coincident TCO from balloon-based ozonesonde measurements. This evaluation was for different tropospheric ozone profile shapes and for different geographical regions (for low, mid, and high latitudes and for Pacific and Atlantic regions). Results indicate that inference on ozone level derived from the satellite-based TCO requires corresponding information about tropospheric ozone profile shape. The use of satellite-based TCO was more appropriate for polluted low-latitude locations where upper troposphere ozone is rare and surface enhanced ozone is high

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches

In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these models were conducted using in-situ observations from the Korea Meteorological Administration (KMA) from 2001 to 2016. The rule of the traditional Koppen-Geiger (K-G) climate classification was used to classify climate regions. The input variables were land surface temperature (LST) of the Moderate Resolution Imaging Spectroradiometer (MODIS), monthly precipitation data from the Tropical Rainfall Measuring Mission (TRMM) 3B43 product, and the Digital Elevation Map (DEM) from the Shuttle Radar Topography Mission (SRTM). The overall accuracy (OA) based on validation data from 2001 to 2016 for all models was high over 95%. DEM and minimum winter temperature were two distinct variables over the study area with particularly high relative importance. ANN produced more realistic spatial distribution of the classified climates despite having a slightly lower OA than the others. The accuracy of the models using high altitudinal in-situ data of the Mountain Meteorology Observation System (MMOS) was also assessed. Although the data length of the MMOS data was relatively short (2013 to 2017), it proved that the snowy, dry and cold winter and cool summer class (Dwc) is widely located in the eastern coastal region of South Korea. Temporal shifting of climate was examined through a comparison of climate maps produced by period: from 1950 to 2000, from 1983 to 2000, and from 2001 to 2013. A shrinking trend of snow classes (D) over the Korean Peninsula was clearly observed from the ANN-based climate classification results. Shifting trends of climate with the decrease/increase of snow (D)/temperate (C) classes were clearly shown in the maps produced using the proposed approaches, consistent with the results from the reanalysis data of the Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC)

Phenological response of vegetation to upstream river flow in the Heihe Rive basin by time series analysis of MODIS data

Liquid and solid precipitation is abundant in the high elevation, upper reach of the Heihe River basin in northwestern China. The development of modern irrigation schemes in the middle reach of the basin is taking up an increasing share of fresh water resources, endangering the oasis and traditional irrigation systems in the lower reach. In this study, the response of vegetation in the Ejina Oasis in the lower reach of the Heihe River to the water yield of the upper catchment was analyzed by time series analysis of monthly observations of precipitation in the upper and lower catchment, river streamflow downstream of the modern irrigation schemes and satellite observations of vegetation index. Firstly, remotely sensed NDVI data acquired by Terra-MODIS are used to monitor the vegetation dynamic for a seven years period between 2000 and 2006. Due to cloud-contamination, atmospheric influence and different solar and viewing angles, however, the quality and consistence of time series of remotely sensed NDVI data are degraded. A Fourier Transform method – the Harmonic Analysis of Time Series (HANTS) algorithm – is used to reconstruct cloud- and noise-free NDVI time series data from the Terra-MODIS NDVI dataset. Modification is made on HANTS by adding additional parameters to deal with large data gaps in yearly time series in combination with a Temporal-Similarity-Statistics (TSS) method developed in this study to seek for initial values for the large gap periods. Secondly, the same Fourier Transform method is used to model time series of the vegetation phenology. The reconstructed cloud-free NDVI time series data are used to study the relationship between the water availability (i.e. the local precipitation and upstream water yield) and the evolution of vegetation conditions in Ejina Oasis from 2000 to 2006. Anomalies in precipitation, streamflow, and vegetation index are detected by comparing each year with the average year. The results showed that: the previous year total runoff had a significant relationship with the vegetation growth in Ejina Oasis and that anomalies in the spring monthly runoff of the Heihe River influenced the phenology of vegetation in the entire oasis. Warmer climate expressed by the degree-days showed positive influence on the vegetation phenology in particular during drier years. The time of maximum green-up is uniform throughout the oasis during wetter years, but showed a clear S-N gradient (downstream) during drier years

The Glacier Complexes of the Mountain Massifs of the North-West of Inner Asia and their Dynamics

The subject of this paper is the glaciation of the mountain massifs Mongun-Taiga, Tavan-Boghd-Ola, Turgeni- Nuru, and Harhira-Nuru. The glaciation is represented mostly by small forms that sometimes form a single complex of domeshaped peaks. According to the authors, the modern glaciated area of the mountain massifs is 21.2 km2 (Tavan-Boghd-Ola), 20.3 km2 (Mongun-Taiga), 42 km2 (Turgeni- Nuru), and 33.1 km2 (Harhira-Nuru). The area of the glaciers has been shrinking since the mid 1960’s. In 1995–2008, the rate of reduction of the glaciers’ area has grown considerably: valley glaciers were rapidly degrading and splitting; accumulation of morainic material in the lower parts of the glaciers accelerated. Small glaciers transformed into snowfields and rock glaciers. There has been also a degradation of the highest parts of the glaciers and the collapse of the glacial complexes with a single zone of accumulation into isolated from each other glaciers. Reduced snow cover area has led to a rise in the firn line and the disintegration of a common accumulation area of the glacial complex. In the of the Mongun-Taiga massif, in 1995– 2008, the firn line rose by 200–300 m. The reduction of the glaciers significantly lagged behind the change in the position of the accumulation area boundary. In the past two years, there has been a significant recovery of the glaciers that could eventually lead to their slower degradation or stabilization of the glaciers in the study area