Spatiotemporal variation of soil moisture in Northern China based on climate change initiative data

2020 ◽  
Author(s):  
X. L. YAO ◽  
Q. JIANG ◽  
Y. LIU ◽  
L. Y. LI ◽  
Q. Y. WANG ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Northern China ◽  
Spatiotemporal Variation ◽  
Change Initiative

scholarly journals Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology

2019 ◽  
Vol 11 (2) ◽  
pp. 717-739 ◽  
Author(s):  
Alexander Gruber ◽  
Tracy Scanlon ◽  
Robin van der Schalie ◽  
Wolfgang Wagner ◽  
Wouter Dorigo
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Scientific Literature ◽  
Passive Microwave ◽  
Climate Data ◽  
Regular Grid ◽  
Change Initiative ◽  
Merging Algorithm ◽  
Merging Algorithms

Abstract. The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978–2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined active–passive product, which are sampled to daily global images on a 0.25∘ regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithms, versions 2 (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c; Wagner et al., 2018), 3 (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c; Dorigo et al., 2018) and 4 (https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e; Dorigo et al., 2019), and provides an outlook on the expected improvements planned for the next algorithm, version 5.

ESA CCI and C3S Soil Moisture: latest product updates and climate assessments

2020 ◽  
Author(s):  
Wouter Dorigo ◽  
Wolfgang Preimesberger ◽  
Adam Pasik ◽  
Alexander Gruber ◽  
Leander Moesinger ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
European Space Agency ◽  
Climate Data ◽  
Change Initiative ◽  
Space Agency ◽  
Data Store ◽  
Climate Data Record ◽  
Level 2

<p>As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a more than 40 year long climate data record (CDR) is produced by systematically combining Level-2 datasets from separate missions. Combining multiple level 2 datasets into a single consistent long-term product combines the advantages of individual missions and allows deriving a harmonised long-term record with optimal spatial and temporal coverage. The current version of ESA CCI Soil Moisture includes a PASSIVE (radiometer-based) dataset covering the period 1978 to 2019, an ACTIVE (scatterometer-based) product covering the period 1991-2019 and a COMBINED product (1978-2019). </p><p>The European Commission’s Copernicus Climate Changes Service (C3S) uses the ESA CCI soil moisture algorithm to produce similar climate data records from near-real-time Level-2 data streams.  These products are continuously extended within 10 days after data acquisition and instantaneously made available through the C3S Climate Data Store. In addition to a daily product, monthly aggregates as well as a dekadal (10-days) products are produced.</p><p>In this presentation we give an overview of the latest developments of the ESA CCI and C3S Soil Moisture datasets, which include the integration of SMAP and various algorithmic updates, and use the datasets to assess the hydrological conditions of 2019 with respect to a 30-year historical baseline.</p><p>The development of the ESA CCI products has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). The Copernicus Climate Change Service (C3S) soil moisture product is funded by the Copernicus Climate Change Service implemented by ECMWF through C3S 312b Lot 7 Soil Moisture service.</p>

scholarly journals Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology

2019 ◽  
Author(s):  
Alexander Gruber ◽  
Tracy Scanlon ◽  
Robin van der Schalie ◽  
Wolfgang Wagner ◽  
Wouter Dorigo
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Scientific Literature ◽  
Passive Microwave ◽  
Climate Data ◽  
Regular Grid ◽  
Change Initiative ◽  
Merging Algorithm

Abstract. The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978–2018) Climate Data Records for soil moisture which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based only, (ii) a passive-microwave-based only, and a combined active-passive product, which are sampled to daily global images on a 0.25 degree regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithm versions 2 (https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c; Wagner et al., 2018), 3 (https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c; Dorigo et al., 2018a), and 4 (https://doi.org/10.5285/3a8a94c3fa464d68b6d70df291afd457; Dorigo et al., 2018b) and provides an outlook to the expected improvements planned for the next algorithm version 5.

ESA CCI and C3S Soil Moisture Products: Generation and Quality Assurance

2021 ◽  
Author(s):  
Tracy Scanlon ◽  
Wouter Dorigo ◽  
Wolfgang Preimesberger ◽  
Robin van der Schalie ◽  
Martin Hirschi ◽  
...  
Keyword(s):  
Climate Change ◽  
Quality Assurance ◽  
Soil Moisture ◽  
Land Surface ◽  
European Space Agency ◽  
Global Scale ◽  
Climate Data ◽  
Change Initiative ◽  
Data Product ◽  
Level 2

<p>Soil moisture Climate Data Records (CDRs) produced from active and passive microwave sensors are valuable for the study of the coupled water, energy and carbon cycles over land on a global scale. As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a multi-decadal CDR is produced by systematically combining Level-2 datasets from separate missions. The combination of individual Level 2 datasets into a single product gives us the opportunity to profit from the advantages of individual missions, and to obtain homogenised CDRs with improved spatial and temporal coverage.<br>The most recent version of the ESA CCI product (v06) provides 3 products: (1978 – 2020), ACTIVE (1991 – 2020) and COMBINED (1978 – 2020). This latest version of the product includes several advances that result in the improved quality of the product. Improvements to the input datasets include updated passive (LPRM – Land Parameter Retrieval Model) data to improve inter-calibration and snow / frozen condition flagging as well as updated ASCAT data from the H-SAF project to improve vegetation correction. <br>Algorithmic improvements include the cross-flagging of snow / frozen conditions to take advantage of the flags provided for each input dataset across all sensors as well as the update of the Signal to Noise Ratio – Vegetation Optical Depth (SNR-VOD) regression used in gap-filling the SNR in locations where retrieval has failed. Additional data is also included through the use of the Global Precipitation Measurement (GPM) mission, the FengYun-3B (FY3B) mission and extending the Tropical Rainfall Measuring Mission (TRMM) dataset used to 2015.<br>An operational product based on the ESA CCI SM product continues to be provided through the EU Copernicus Climate Changes Services (C3S) Climate Data Store (CDS). This operational product provides daily data and decadal (10 daily) aggregates in near-real-time as well as monthly aggregates for the historical dataset. The anomalies derived from this dataset (with a base period of 1991 to 2010) can be seen on the TU Wien data viewer (https://dataviewer.geo.tuwien.ac.at/).<br>The accuracy of each data product is assessed through comparison to in-situ soil moisture observations from the International Soil Moisture Network (ISMN) as well as modelled data from Land Surface Models (LSMs). Such assessments are undertaken each time a new ESA CCI version is generated, and the results compared against previous versions to assess the evolution of the product quality over time. For transparency and traceability, an online portal is provided for the public to perform similar validations (Quality Assurance for Soil Moisture – www.qa4sm.eu). <br>In this study, an overview of the product generation and the updates provided at ESA CCI SM v06 is presented as well as examples of how the data product has been used. The associated quality assurance requirements, assessment procedures and results will also be presented.<br>The development of the ESA CCI products has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). Funded by Copernicus Climate Change Service implemented by ECMWF through C3S 312a Lot 7 Soil Moisture service.</p>

scholarly journals Spatiotemporal Variation of Land Surface Temperature and Vegetation in Response to Climate Change Based on NOAA-AVHRR Data over China

2020 ◽  
Vol 12 (9) ◽  
pp. 3601
Author(s):  
Zhaoqi Wang ◽  
Zhiyuan Lu ◽  
Guolong Cui
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Global Climate Change ◽  
Land Surface ◽  
Driving Forces ◽  
Global Climate ◽  
Northern China ◽  
Spatiotemporal Variation ◽  
Noaa Avhrr

The dynamics of land surface temperature (LST) and its correlation with vegetation are crucial to understanding the effects of global climate change. This study intended to retrieve the LST of China, based on the NOAA-AVHRR images, by using a split-window algorithm. The spatiotemporal variation of LST, Normalized difference vegetation index (NDVI), and the correlation between the two was investigated in China from 1982–2016. Moreover, eight scenarios were established to explore the driving forces in vegetation variation. Results indicated that the LST increased by 0.06 °C/year in nearly 81.1% of the study areas. The NDVI with an increasing rate of 0.1%/year and occupied 58.6% of the study areas. By contrast, 41.4% of the study areas with a decreasing rate of 0.7 × 10−3/year, was mainly observed in northern China. The correlation coefficients between NDVI and LST were higher than that between NDVI and precipitation, and the increase in LST could stimulate vegetation growth. Most regions of China have experienced significant warming over the past decades, specifically, desertification happens in northern China, because it is getting drier. The synergy of LST and precipitation is the primary cause of vegetation dynamics. Therefore, long-term monitoring of LST and NDVI is necessary to better understand the adaptation of the terrestrial ecosystem to global climate change.

scholarly journals Projecting heat-related excess mortality under climate change scenarios in China

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jun Yang ◽  
Maigeng Zhou ◽  
Zhoupeng Ren ◽  
Mengmeng Li ◽  
Boguang Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Excess Mortality ◽  
Climate Models ◽  
Global Climate ◽  
Specific Effect ◽  
Northern China ◽  
The Elderly ◽  
Population Ageing ◽  
Global Climate Models ◽  
Climate Change Scenarios

AbstractRecent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2–3.3%) in the 2010s to 2.4% (0.4–4.1%) in the 2030 s and 5.5% (0.5–9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0–1.2%) and 3.6% (−0.5–7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change.

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