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.

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: 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>

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 The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals Climate Change Influences Basidiome Emergence of Leaf-Cutting Ant Cultivars

2021 ◽  
Vol 7 (11) ◽  
pp. 912
Author(s):  
Rodolfo Bizarria ◽  
Pepijn W. Kooij ◽  
Andre Rodrigues
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Nest Architecture ◽  
Historical Records ◽  
Climate Data ◽  
Temperature And Precipitation ◽  
Data Analyses ◽  
Symbiotic Mutualism ◽  
Leaf Cutting

Maintaining symbiosis homeostasis is essential for mutualistic partners. Leaf-cutting ants evolved a long-term symbiotic mutualism with fungal cultivars for nourishment while using vertical asexual transmission across generations. Despite the ants’ efforts to suppress fungal sexual reproduction, scattered occurrences of cultivar basidiomes have been reported. Here, we review the literature for basidiome occurrences and associated climate data. We hypothesized that more basidiome events could be expected in scenarios with an increase in temperature and precipitation. Our field observations and climate data analyses indeed suggest that Acromyrmex coronatus colonies are prone to basidiome occurrences in warmer and wetter seasons. Even though our study partly depended on historical records, occurrences have increased, correlating with climate change. A nest architecture with low (or even the lack of) insulation might be the cause of this phenomenon. The nature of basidiome occurrences in the A. coronatus–fungus mutualism can be useful to elucidate how resilient mutualistic symbioses are in light of climate change scenarios.

scholarly journals Varying soil moisture-atmosphere feedbacks explain divergent temperature extremes and precipitation projections in Central Europe

2018 ◽  
Author(s):  
Martha M. Vogel ◽  
Jakob Zscheischler ◽  
Sonia I. Seneviratne
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Central Europe ◽  
Climate Models ◽  
Global Climate ◽  
Summer Precipitation ◽  
Global Climate Models ◽  
Temperature Extremes ◽  
Extreme Temperatures

Abstract. The frequency and intensity of climate extremes is expected to increase in many regions due to anthropogenic climate change. In Central Europe extreme temperatures are projected to change more strongly than global mean temperatures and soil moisture-temperature feedbacks significantly contribute to this regional amplification. Because of their strong societal, ecological and economic impacts, robust projections of temperature extremes are needed. Unfortunately, in current model projections, temperature extremes in Central Europe are prone to large uncertainties. In order to understand and potentially reduce uncertainties of extreme temperatures projections in Europe, we analyze global climate models from the CMIP5 ensemble for the business-as-usual high-emission scenario (RCP8.5). We find a divergent behavior in long-term projections of summer precipitation until the end of the 21st century, resulting in a trimodal distribution of precipitation (wet, dry and very dry). All model groups show distinct characteristics for summer latent heat flux, top soil moisture, and temperatures on the hottest day of the year (TXx), whereas for net radiation and large-scale circulation no clear trimodal behavior is detectable. This suggests that different land-atmosphere coupling strengths may be able to explain the uncertainties in temperature extremes. Constraining the full model ensemble with observed present-day correlations between summer precipitation and TXx excludes most of the very dry and dry models. In particular, the very dry models tend to overestimate the negative coupling between precipitation and TXx, resulting in a too strong warming. This is particularly relevant for global warming levels above 2 °C. The analysis allows for the first time to substantially reduce uncertainties in the projected changes of TXx in global climate models. Our results suggest that long-term temperature changes in TXx in Central Europe are about 20 % lower than projected by the multi-model median of the full ensemble. In addition, mean summer precipitation is found to be more likely to stay close to present-day levels. These results are highly relevant for improving estimates of regional climate-change impacts including heat stress, water supply and crop failure for Central Europe.

scholarly journals The rate of ecological acclimation is the dominant uncertainty in long-term projections of a key ecosystem service

2021 ◽  
Author(s):  
Andrew J. Felton ◽  
Robert K. Shriver ◽  
Michael Stemkovski ◽  
John B. Bradford ◽  
Katharine N. Suding ◽  
...  
Keyword(s):  
Climate Change ◽  
Ecosystem Services ◽  
Greenhouse Gas Emission ◽  
Species Turnover ◽  
Spatial Gradient ◽  
Climate Data ◽  
Forage Production ◽  
Global Circulation Models ◽  
Decadal Scale

The potential for ecosystems to continue providing society with essential services may depend on their ability to acclimate to climate change through multiple processes operating from cells to landscapes. While models to predict climate change impacts on ecosystem services often consider uncertainty among greenhouse gas emission scenarios or global circulation models (GCMs), they rarely consider the rate of ecological acclimation, which depends on decadal-scale processes such as species turnover. Here we show that uncertainty due to the unknown rate of ecological acclimation is larger than other sources of uncertainty in late-century projections of forage production in US rangelands. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous productivity with an ensemble of GCMs, we projected changes in forage production using two approaches. The time-series approach, which assumes minimal acclimation, projects widespread decreases in forage production. The spatial gradient approach, which assumes ecological acclimation keeps pace with climate, predicts widespread increases in forage production. This first attempt to quantify the magnitude of a critical uncertainty emphasizes that better understanding of ecological acclimation is essential to improve long-term forecasts of ecosystem services, and shows that management to facilitate ecological acclimation may be necessary to maintain ecosystem services at historical baselines.

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