全球气候数据集生成及气候变化应用研究

科技部在"十三五"期间部署的国家重点研发计划"全球变化及应对"专项资助了"全球气候数据集生成及气候变化关键过程和要素监测"研究项目。项目围绕由全球气候观测系统提出的基本气候变量,完善地空天基观测体系,生成中国首套以遥感数据为主体的涵盖大气、海洋和陆表长时间序列、高精度、高时空一致性的产品,即气候数据集,动态监测全球变化关键过程和要素。     

Reviewing the discoverability and accessibility to data and information products linked to Essential Climate Variables

ABSTRACT

Essential Climate Variables (ECVs) are geophysical records generated from systematic Earth Observations associated with climate variations, changes, and impacts. ECVs products support the data and information needs of international frameworks and policies such as the work of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC). We map the main networks and initiatives publishing ECVs, by presenting an overview of existing satellite-based ECVs, their general data creation characteristics, discoverability and accessibility methods from an end-user perspective. We investigate key initiatives providing or coordinating access to ECV data records, such as the Global Climate Observing System (GCOS), the Committee on Earth Observation Satellites (CEOS), the Coordination Group for Meteorological Satellites (CGMS), Joint Working Group on Climate (WGClimate), the Remote Sensing Systems (REMSS), and the European Space Agency Climate Change Initiative (ESA CCI). We find that ECV data discovery and access is difficult and time consuming due to the lack of common data and metadata catalogues. In addition, the selection of fit-for-purpose data records by end-users requires the implementation of interoperable standards and scalable data infrastructures to allow the generation of tailored applications and data-driven information products in support of decision-making processes.     

Satellite-observed changes in terrestrial vegetation growth trends across the Asia-Pacific region associated with land cover and climate from 1982 to 2011

The Asia-Pacific (AP) region has experienced faster warming than the global average in recent decades and has experienced more climate extremes, however little is known about the response of vegetation growth to these changes. The updated Global Inventory Modeling and Mapping Studies third-generation global satellite Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index dataset and gridded reanalysis climate data were used to investigate the spatiotemporal changes in both trends of vegetation dynamic indicators and climatic variables. We then further analyzed their relations associated with land cover across the AP region. The main findings are threefold: (1) at continental scales the AP region overall experienced a gradual and significant increasing trend in vegetation growth during the last three decades, and this NDVI trend corresponded with an insignificant increasing trend in temperature; (2) vegetation growth was negatively and significantly correlated with the Pacific Decadal Oscillation index and the El Niño/Southern Oscillation (ENSO) in AP; and (3) at pixel scales, except for Australia, both vegetation growth and air temperature significantly increased in the majority of study regions and vegetation growth spatially correlated with temperature; In Australia and other water-limited regions vegetation growth positively correlated with precipitation.     

Basin-wide hydrological system assessment under climate change scenarios through conceptual modelling

ABSTRACT

Mediterranean region is identified as a primary hot-spot for climate change due to the expected temperature and rainfall changes. Understanding the potential impacts of climate change on the hydrology in these regions is an important task to develop long-term water management strategies. The aim of this study was to quantify the potential impacts of the climate changes on local hydrological quantities at the Goksu Watershed at the Eastern Mediterranean, Turkey as a case study. A set of Representative Concentration Pathways (RCP) scenarios were used as drivers for the conceptual hydrological model J2000 to investigate how the hydrological system and the underlying processes would respond to projected future climate conditions. The model was implemented to simulate daily hydrological quantities including runoff generation, Actual Evapotranspiration (AET) and soil-water balance for present (2005–2015) and future (up to 2100). The results indicated an increase of both precipitation and runoff throughout the region from January to March. The region showed a strong seasonally dependent runoff regime with higher flows during winter and spring and lower flows in summer and fall. The study provides a comparative methodology to include meteorological-hydrological modelling integration that can be feasible to assess the climate change impacts in mountainous regions.     

基于Web地理可视化分析系统的研究与设计

气候研究涉及海量时空数据集,其产生和存储在分布式计算资源中。科学家们需要一个直观且方便地工具去研究分布式时空数据。地理可视化分析工具可直观且方便地访问气候资料、探索各种气候参数之间的关系及交流研究成果。本文对基于Web的地理可视化分析系统的研究与设计做一些探讨,阐述该系统具有以下功能：①互联网上海量数据集管理;②时空数据的2D/3D可视化;③气候研究中各种时空统计分析;④交互式数据分析和知识发现。此外,本文也为管理、分发、分析大数据提供参考。     

青藏高原地表冻融循环与植被返青期的变化趋势及其气候响应特征

青藏高原特殊的地理环境使其对全球气候变化十分敏感,所以研究其地表冻融循环和植被返青期的时空动态对于回顾和预测青藏高原对全球气候变化的响应具有重要意义。本文通过利用双指标地表冻融状态识别算法和被动微波亮温数据(SMMR、SSMI和SSMIS)来获取青藏高原长时间序列(1982年—2013年)逐日地表冻融状态,通过对GIMMS全球植被指数数据产品进行NDVI的滤波重建和返青期提取来获取青藏高原植被长时间序列(年份)的返青期;并且分析了地表冻融循环和植被返青期的变化趋势、相互关系及对青藏高原气候变化的响应特征。总体来看,在空间上,青藏高原的地表冻结集中发生在10月30日至次年4月2日,平均地表融化首日集中在5月12—27日,平均植被返青期集中在5月19—29日。植被返青期平均发生在地表融化首日后的3.94±5.58日,两者具有显著的相关关系(R=0.51,P=0.003)。青藏高原的地表融化首日和植被返青期在1982年—2013年间经历了推迟、提前再推迟的3个过程,融化时间和返青期在1982年—1987年分别以1.93±1.81 d/a和0.28±1.01 d/a的速度推迟;在1987年—2006年分别以0.67±0.20 d/a和0.13±0.16 d/a的速度提前;在2006年—2013年分别以0.97±0.84 d/a和1.04±0.52 d/a的速度推迟。中国气象局布设在青藏高原的CMA气象站的温度数据表明,高原的春季地表0 cm土壤温度呈持续上升的趋势,而植被返青期和地表融化首日并未持续提前,这可能是由几十年来高原不同地区降水等其他环境因素变化的差异造成。同时在气温持续升高期间,植被返青期的返青温度阈值也不断具有上升的趋势(R=0.72,P0.001),这可能与植被适应气候变化的自身调节能力有关。     

Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r² = 0.58) and at a 50-cm depth (r² = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.     

Pacific climate variability and the possible impact on global surface CO2 flux

Background  

Climate variability modifies both oceanic and terrestrial surface CO2 flux. Using observed/assimilated data sets, earlier studies have shown that tropical oceanic climate variability has strong impacts on the land surface temperature and soil moisture, and that there is a negative correlation between the oceanic and terrestrial CO2 fluxes. However, these data sets only cover less than the most recent 20 years and are insufficient for identifying decadal and longer periodic variabilities. To investigate possible impacts of interannual to interdecadal climate variability on CO2 flux exchange, the last 125 years of an earth system model (ESM) control run are examined.     

Variational data assimilation for deriving global climate analyses from GNSS radio occultation data

A comprehensive global navigation satellite system (GNSS) based radio occultation (RO) data set is available for meteorology and climate applications since the start of GNSS RO measurements aboard the CHAllenging Mini-satellite Payload (CHAMP) satellite in February 2001. Global coverage, all-weather capability, long-term stability and accuracy not only makes this innovative use of GNSS signals a valuable supplement to the data set assimilated into numerical weather prediction (NWP) systems but also an excellent candidate for global climate monitoring. We present a 3D variational data assimilation (3D-Var) scheme developed to derive consistent global analysis fields of temperature, specific humidity, and surface pressure from GNSS RO data. The system is based on the assimilation of RO data within 6 h time windows into European Centre for Medium-Range Weather Forecasts (ECMWF) short-term (24 h, 30 h) forecasts, to derive climatologic monthly mean fields. July 2003 was used as a test-bed for assessing the system’s performance. The results show good agreement with climatologies derived from RO data only and recent NWP impact studies. These findings are encouraging for future developments to apply the approach for longer term climatologic analyses, validation of other data sets, and atmospheric variability studies.     

Identifying coastal towns and small cities in Denmark using global population data to support climate change adaptation

ABSTRACT

Coastal settlements face many hazards from climate change. Consequently, there has been extensive focus on developing and implementing adaptation. However, these efforts have prodominantly centred on larger cities. Coastal towns and small cities (urban areas between 1000 and 100,000 people) have received little attention, despite experiencing a number of barriers to adaptation. The absence of information on the global scale of the adaptation challenge within coastal towns and small cities may have contributed to these settlements being overlooked. This paper develops a method that can be used to estimate the numbers, sizes, and locations of coastal towns and small cities worldwide from global population data (Global Human Settlement data). Denmark is used as a pilot for this method with settlements over 1000 people classified with relatively high accuracy. The method developed here represents a potentially fruitful approach to supporting coastal adaptation, as coastal towns and small cities are identifiable globally, they can be classified into types. This will support an assessment of their risk to coastal hazards, and could facilitate knowledge and practice sharing between similar coastal towns and small cities.     

Glacier changes using satellite data and effect of climate in Tirungkhad basin located in western Himalaya

Glaciers are widely recognized as key indicators of climate change, and melt water obtained from them is an important source of fresh water and for hydropower generation. Regular monitoring of a large number of Himalayan glaciers is important for improving our knowledge of glacier response to climate change. In the present study, Survey of India topographical maps (1966) and Landsat datasets as ETM+ (2000, 2006) and TM (2011) have been used to study glacier fluctuations in Tirungkhad basin. A deglaciation of 26.1% (29.1?km²) in terms of area from 1966 to 2011 was observed. Lower altitude small glaciers (area?<?1?km²) lost more ice (34%), while glaciers with an area <10?km² lost less (20%). The percentage of change in glacier length was 26% (31.9?km) from 1966 to 2011. The south-facing glaciers showed high percentages of loss. From 2000 to 2011, debris cover has increased by 1.34%. The analysis of the trend in meteorological data collected from Kalpa and Purbani stations was carried out by Mann Kendall non-parametric method. During the last two decades, the mean annual temperature (T_max and T_min) has increased significantly, accompanied with a fall in snow water equivalent (SWE) and rainfall. The increasing trend in temperature and decreasing trend in SWE were significant at 95% confidence level. This observation shows that the warming of the climate is probably one of the major reasons for the glacier change in the basin.     

Climate applications of a global, 2-hourly atmospheric precipitable water dataset derived from IGS tropospheric products

A global, 2-hourly atmospheric precipitable water (PW) dataset is produced from ground-based GPS measurements of zenith tropospheric delay (ZTD) using the International Global Navigation Satellite Systems (GNSS) Service (IGS) tropospheric products (~80–370 stations, 1997–2006) and US SuomiNet product (169 stations, 2003–2006). The climate applications of the GPS PW dataset are highlighted in this study. Firstly, the GPS PW dataset is used as a reference to validate radiosonde and atmospheric reanalysis data. Three types of systematic errors in global radiosonde PW data are quantified based on comparisons with the GPS PW data, including measurement biases for each of the fourteen radiosonde types along with their characteristics, long-term temporal inhomogeneity and diurnal sampling errors of once and twice daily radiosonde data. The comparisons between the GPS PW data and three reanalysis products, namely the NCEP-NCAR (NNR), ECMWF 40-year (ERA-40) and Japanese reanalyses (JRA), show that the elevation difference between the reanalysis grid box and the GPS station is the primary cause of the PW difference. Secondly, the PW diurnal variations are documented using the 2-hourly GPS PW dataset. The PW diurnal cycle has an annual-mean, peak-to-peak amplitude of 0.66, 0.53 and 1.11 mm for the globe, Northern Hemisphere, and Southern Hemisphere, respectively, with the time of the peak ranging from noon to late evening depending on the season and region. Preliminary analyses suggest that the PW diurnal cycle in Europe is poorly represented in the NNR and JRA products. Several recommendations are made for future improvements of IGS products for climate applications.     

Characterizing and monitoring global landscapes using GlobeLand30 datasets: the first decade of the twenty-first century

Global land cover maps are important sources of information for a wide range of studies including land change analysis and climate change research. While the global land cover maps attempt to present a consistent and homogenous data in terms of the production process, the existing datasets offer coarse resolution data, e.g. 1000 m for IGBP DISCover and 300 m for GlobeCover 2009 that is oftentimes challenging. Recently, GlobeLand30 data based on Landsat archive for two timestamps of 2000 and 2010 has been released. It presents a finer spatial resolution of 30 m, which provides numerous opportunities for a wide range of studies. The main objective of this study is to use this dataset for characterizing global land cover patterns, monitoring, and identifying extreme land change cases with their types and magnitude. The findings reveal massive land change patterns including deforestation, desertification, shrinkage of water bodies, and urbanization across the globe. The results and discussions of this research can help policy-makers, environmental planners, ecosystem services providers and climate change researchers to gain finer insights about the forms of global land change. Future research calls for further investigation of the underlying causes of the massive changes and their consequences on our ecosystems and human populations.     

Functional relation of land surface albedo with climatological variables: a review on remote sensing techniques and recent research developments

Surface albedo has been documented as one of the Essential Climate Variables (ECV) of the Global Climate Observing System (GCOS) that governs the Earth's Radiation Budget. The availability of surface albedo data is necessary for a comprehensive environmental modelling study. Thus, both temporal and spatial scale issues need to be rectified. This study reports about the availability of surface albedo data through in-situ and remote sensing satellite observations. In this paper, we reviewed the existing models for surface albedo derivation and various initiatives taken by related environmental agencies in order to understand the issues of climate with respect to surface albedo. This investigation evaluated the major activities on albedo-related research specifically for the retrieval methods used to derive the albedo values. Two main existing albedo measurement methods are derived through in-situ measurement and remotely sensed observations. In-situ measurement supported with number of instruments and techniques such aspyrheliometers, pyranometers and Baseline Surface Radiation Network (BSRN) and remotely sensed observations using angularly integrated Bi-directional Reflectance Distribution Function (BRDF) by both geostationary and polar orbit satellites. The investigation results reveals that the temporal and spatial scaling is the major issues when the albedo values are needed for microclimatic study, i.e. high-resolution time-series analyses and at heterogeneity and impervious surface. Thus, an improved technique of albedo retrieval at better spatial and temporal scale is required to fulfil the need for such kind of studies. Amongst many others, there are two downscaling methods that have been identified to be used in resolving the spatial scaling biased issues: Smoothing Filter-based Intensity Modulation (SFIM) and Pixel Block Intensity Modulation (PBIM). The temporal issues can be resolved using the multiple regression techniques of land surface temperature, selected air quality parameters, aerosol and daily skylight.     

Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data.     

A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability

We develop a new algorithm, the simplified urban-extent (SUE) algorithm, to estimate the surface urban heat island (UHI) intensity at a global scale. We implement the SUE algorithm on the Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer (MODIS) images to calculate the UHI intensity for over 9500 urban clusters using over 15 years of data, making this one of the most comprehensive characterizations of the surface UHI to date. The results from this algorithm are validated against previous multi-city studies to demonstrate the suitability of the method. The dataset created is then filtered for elevation differentials and percentage of urban area and used to estimate the diurnal, monthly, and long-term variability in the surface UHI in different climate zones. The global mean surface UHI intensity is 0.85 °C during daytime and 0.55 °C at night. Cities in arid climate show distinct diurnal and seasonal patterns, with higher surface UHI during nighttime (compared to daytime) and two peaks throughout the year. The diurnal variability in surface UHI is highest for equatorial climate zone (0.88 °C) and lowest for arid zone (0.53 °C). The seasonality is highest in the snow climate zone and lowest for equatorial climate zone. While investigating the change in the surface UHI over a decade and a half, we find a consistent increase in the daytime surface UHI in the urban clusters of the warm temperate climate zone (0.04 °C/decade) and snow climate zone (0.05 °C/decade). Only arid climate zones show a statistically significant increase in the nighttime surface UHI intensity (0.03 °C/decade). Globally, the change is mainly seen during the daytime (0.03 °C/decade). Finally, the importance of vegetation differential between urban and rural areas on the spatiotemporal variability is examined. Vegetation has a strong control on the seasonal variability of the surface UHI and may also partly control the long-term variability. The complete UHI data are available through this website (https://yceo.yale.edu/research/global-surface-uhi-explorer) and allows the user to query the UHI of urban clusters using a simple interface.     

Responses of global terrestrial water use efficiency to climate change and rising atmospheric CO2 concentration in the twenty-first century

Terrestrial ecosystems play a significant role in global carbon and water cycles because of the substantial amount of carbon assimilated through net primary production and large amount of water loss through evapotranspiration (ET). Using a process-based ecosystem model, we investigate the potential effects of climate change and rising atmospheric CO₂ concentration on global terrestrial ecosystem water use efficiency (WUE) during the twenty-first century. Future climate change would reduce global WUE by 16.3% under high-emission climate change scenario (A2) and 2.2% under low-emission climate scenario (B1) during 2010–2099. However, the combination of rising atmospheric CO₂ concentration and climate change would increase global WUE by 7.9% and 9.4% under A2 and B1 climate scenarios, respectively. This suggests that rising atmospheric CO₂ concentration could ameliorate climate change-induced WUE decline. Future WUE would increase significantly at the high-latitude regions but decrease at the low-latitude regions under combined changes in climate and atmospheric CO₂. The largest increase of WUE would occur in tundra and boreal needleleaf deciduous forest under the combined A2 climate and atmospheric CO₂ scenario. More accurate prediction of WUE requires deeper understanding on the responses of ET to rising atmospheric CO₂ concentrations and its interactions with climate.     

Global analysis of time-lag and -accumulation effects of climate on vegetation growth

Climate dominantly controls vegetation over most regions at most times, and vegetation responses to climate change are often asymmetric with temporal effects. However, systematic analysis of the time-lag and time-accumulation effects of climate on vegetation growth, has rarely been conducted, in particular for different vegetation growing phases. Thus, this study aimed to leverage normalized difference vegetation index (NDVI) to determine the spatiotemporal patterns of climatic effects on global vegetation growth considering various scenarios of time-lag and/or accumulation effects. The results showed that (i) climatic factors have time-lag and -accumulation effects as well as their combined effects on global vegetation growth for the whole growing season and its subphases (i.e., the growing and senescent phases). However, these effects vary with climatic factors, vegetation types, and regions. Compared with those of temperature, both precipitation and solar radiation display more significant time-accumulation effects in the whole growing season worldwide, but behave differently in the growing and senescent phases in the middle-high latitudes of the Northern Hemisphere; (ii) compared to the scenario without time effects, considering time-lag and -accumulation effects as well as their combined effects increased by 17 %, 15 %, and 19 % the overall explanatory power of vegetation growth by climate change for the whole growing season, the growing phase, and senescent phase, respectively; (iii) considering the time-lag and -accumulation effects as well as their combined effects, climate change controls 70 % of areas with a significant NDVI variation from 1982 to 2015, and the primary driving factor was temperature, followed by solar radiation and precipitation. This study highlights the significant time-lag and -accumulation effects of climatic factors on global vegetation growth. We suggest that these effects need to be incorporated into dynamic vegetation models to better understand vegetation growth under accelerating climate change.     

Production of global land cover data – GLCNMO

Abstract

Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and expert's comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.     

The potential impact of climate change on Australia's soil organic carbon resources

Background  

Soil organic carbon (SOC) represents a significant pool of carbon within the biosphere. Climatic shifts in temperature and precipitation have a major influence on the decomposition and amount of SOC stored within an ecosystem and that released into the atmosphere. We have linked net primary production (NPP) algorithms, which include the impact of enhanced atmospheric CO₂ on plant growth, to the SOCRATES terrestrial carbon model to estimate changes in SOC for the Australia continent between the years 1990 and 2100 in response to climate changes generated by the CSIRO Mark 2 Global Circulation Model (GCM).