scholarly journals Marine Heatwaves in the Chesapeake Bay

2022 ◽  
Vol 8 ◽  
Author(s):  
Piero L. F. Mazzini ◽  
Cassia Pianca
Keyword(s):  
Chesapeake Bay ◽  
Large Scale ◽  
Sea Water ◽  
Benthic Communities ◽  
Data Set ◽  
Modes Of Variability ◽  
The Us ◽  
Temperature Records

Prolonged events of anomalously warm sea water temperature, or marine heatwaves (MHWs), have major detrimental effects to marine ecosystems and the world's economy. While frequency, duration and intensity of MHWs have been observed to increase in the global oceans, little is known about their potential occurrence and variability in estuarine systems due to limited data in these environments. In the present study we analyzed a novel data set with over three decades of continuous in situ temperature records to investigate MHWs in the largest and most productive estuary in the US: the Chesapeake Bay. MHWs occurred on average twice per year and lasted 11 days, resulting in 22 MHW days per year in the bay. Average intensities of MHWs were 3°C, with maximum peaks varying between 6 and 8°C, and yearly cumulative intensities of 72°C × days on average. Large co-occurrence of MHW events was observed between different regions of the bay (50–65%), and also between Chesapeake Bay and the Mid-Atlantic Bight (40–50%). These large co-occurrences, with relatively short lags (2–5 days), suggest that coherent large-scale air-sea heat flux is the dominant driver of MHWs in this region. MHWs were also linked to large-scale climate modes of variability: enhancement of MHW days in the Upper Bay were associated with the positive phase of Niño 1+2, while enhancement and suppression of MHW days in both the Mid and Lower Bay were associated with positive and negative phases of North Atlantic Oscillation, respectively. Finally, as a result of long-term warming of the Chesapeake Bay, significant trends were detected for MHW frequency, MHW days and yearly cumulative intensity. If these trends persist, by the end of the century the Chesapeake Bay will reach a semi-permanent MHW state, when extreme temperatures will be present over half of the year, and thus could have devastating impacts to the bay ecosystem, exacerbating eutrophication, increasing the severity of hypoxic events, killing benthic communities, causing shifts in species composition and decline in important commercial fishery species. Improving our basic understanding of MHWs in estuarine regions is necessary for their future predictability and to guide management decisions in these valuable environments.

scholarly journals A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies

2016 ◽  
Vol 8 (1) ◽  
pp. 165-176 ◽  
Author(s):  
Viva Banzon ◽  
Thomas M. Smith ◽  
Toshio Mike Chin ◽  
Chunying Liu ◽  
William Hankins
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Sea Surface ◽  
Climate Data ◽  
Data Set ◽  
Volcanic Aerosols ◽  
Climate Data Record

Abstract. This paper describes a blended sea-surface temperature (SST) data set that is part of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program product suite. Using optimum interpolation (OI), in situ and satellite observations are combined on a daily and 0.25° spatial grid to form an SST analysis, i.e., a spatially complete field. A large-scale bias adjustment of the input infrared SSTs is made using buoy and ship observations as a reference. This is particularly important for the time periods when volcanic aerosols from the El Chichón and Mt. Pinatubo eruptions are widespread globally. The main source of SSTs is the Advanced Very High Resolution Radiometer (AVHRR), available from late 1981 to the present, which is also the temporal span of this CDR. The input and processing choices made to ensure a consistent data set that meets the CDR requirements are summarized. A brief history and an explanation of the forward production schedule for the preliminary and science-quality final product are also provided. The data set is produced and archived at the newly formed National Centers for Environmental Information (NCEI) in Network Common Data Form (netCDF) at doi:10.7289/V5SQ8XB5.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

In Situ Tests on Creep Behavior of Rock Mass with Joint or Shearing Zone in Foundation of Large-Scale Hydroelectric Projects

2004 ◽  
Vol 261-263 ◽  
pp. 1097-1102 ◽  
Author(s):  
Jian Liu ◽  
Xia Ting Feng ◽  
Xiu Li Ding ◽  
Huo Ming Zhou
Keyword(s):  
Rock Mass ◽  
Creep Behavior ◽  
Large Scale ◽  
Time Dependent ◽  
Three Gorges Project ◽  
Three Gorges ◽  
The Three Gorges ◽  
The Three Gorges Project

The time-dependent behavior of rock mass, which is generally governed by joints and shearing zones, is of great significance for engineering design and prediction of long-term deformation and stability. In situ creep test is a more effective method than laboratory test in characterizing the creep behavior of rock mass with joint or shearing zone due to the complexity of field conditions. A series of in situ creep tests on granite with joint at the shiplock area of the Three-Gorges Project and basalt with shearing zone at the right abutment of the Xiluodu Project were performed in this study. Based on the test results, the stress-displacement-time responses of the joints and basalt are analyzed, and their time-dependent constitutive model and model coefficients are given, which is crucial for the design to prevent the creep deformations of rock masses from causing the failure of the operation of the shiplock gate at the Three-Gorges Project and long-term stability of the Xiluodu arc dam.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 27
Author(s):  
Lahouari Bounoua ◽  
Kurtis Thome ◽  
Joseph Nigro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Climate Mitigation ◽  
Temperature Changes ◽  
Surface Warming ◽  
The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

scholarly journals ENVIRONMENTAL POLLUTION BY WASTEWATER FROM BROWN COAL PROCESSING ¬ A REMEDIATION CASE STUDY IN GERMANY

2014 ◽  
Vol 22 (1) ◽  
pp. 71-83 ◽  
Author(s):  
Arndt Wiessner ◽  
Jochen A. Müller ◽  
Peter Kuschk ◽  
Uwe Kappelmeyer ◽  
Matthias Kästner ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Large Scale ◽  
Pilot Scale ◽  
Wastewater Disposal ◽  
Interdisciplinary Approaches ◽  
Phenolic Wastewater ◽  
Term Monitoring

The large scale of the contamination by the former carbo-chemical industry in Germany requires new and often interdisciplinary approaches for performing an economically sustainable remediation. For example, a highly toxic and dark-colored phenolic wastewater from a lignite pyrolysis factory was filled into a former open-cast pit, forming a large wastewater disposal pond. This caused an extensive environmental pollution, calling for an ecologically and economically acceptable strategy for remediation. Laboratory-scale investigations and pilot-scale tests were carried out. The result was the development of a strategy for an implementation of full-scale enhanced in situ natural attenuation on the basis of separate habitats in a meromictic pond. Long-term monitoring of the chemical and biological dynamics of the pond demonstrates the metamorphosis of a former highly polluted industrial waste deposition into a nature-integrated ecosystem with reduced danger for the environment, and confirmed the strategy for the chosen remediation management.

scholarly journals Two decades of in-situ temperature measurements in the upper troposphere and lowermost stratosphere from IAGOS long-term routine observation

2017 ◽  
Author(s):  
Florian Berkes ◽  
Patrick Neis ◽  
Martin G. Schultz ◽  
Ulrich Bundke ◽  
Susanne Rohs ◽  
...  
Keyword(s):  
Weather Forecast ◽  
Global Scale ◽  
Temperature Trend ◽  
Data Set ◽  
Medium Range Weather Forecast ◽  
Upper Troposphere ◽  
Temperature Trends ◽  
Geographical Regions

Abstract. Despite several studies on temperature trends in the tropopause region, a comprehensive understanding of the evolution of temperatures in this climate-sensitive region of the atmosphere remains elusive. Here we present a unique global-scale, long-term data set of high-resolution in-situ temperature data measured aboard passenger aircraft within the European Research Infrastructure IAGOS (In-service Aircraft for a Global Observing System, www.iagos.org). This data set is used to investigate temperature trends within the global upper troposphere and lowermost stratosphere (UTLS) for the period 1995 to 2012 in different geographical regions and vertical layers of the UTLS. The largest amount of observations is available over the North Atlantic. Here, a neutral temperature trend is found within the lowermost stratosphere. This contradicts the temperature trend in the European Centre for Medium Range Weather Forecast (ECMWF) ERA-Interim reanalysis, where a significant (95 % confidence) temperature increase of +0.56 K/decade is obtained. Differences between trends derived from observations and reanalysis data can be traced back to changes in the temperature bias between observation and model data over the studied period. This study demonstrates the value of the IAGOS temperature observations as anchor point for the evaluation of reanalyses and its suitability for independent trend analyses.

Sea surface salinity reemergence in an updated North Atlantic in-situ salinity data set

2021 ◽  
pp. 1-49
Author(s):  
Claude Frankignoul ◽  
Elodie Kestenare ◽  
Gilles Reverdin
Keyword(s):  
Large Scale ◽  
Cold Season ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Data Set ◽  
Salinity Data ◽  
Deep Mixed Layer ◽  
Geostrophic Advection

AbstractMonthly sea surface salinity (SSS) fields are constructed from observations, using objective mapping on a 1°x1° grid in the Atlantic between 30°S and 50°N in the 1970-2016 period in an update of the data set of Reverdin et al. (2007). Data coverage is heterogeneous, with increased density in 2002 when Argo floats become available, high density along Voluntary Observing Ship lines, and low density south of 10°S. Using lag correlation, the seasonal reemergence of SSS anomalies is investigated between 20°N and 50°N in 5°x5° boxes during the 1993-2016 period, both locally and remotely following the displacements of the deep mixed-layer waters estimated from virtual float trajectories derived from the daily AVISO surface geostrophic currents. Although SSS data are noisy, local SSS reemergence is detected in about half of the boxes, notably in the northeast and southeast, while little reemergence is seen in the central and part of the eastern subtropical gyre. In the same period, sea surface temperature (SST) reemergence is found only slightly more frequently, reflecting the short data duration. However, taking geostrophic advection into account degrades the detection of remote SSS and even SST reemergence. When anomalies are averaged over broader areas, robust evidence of a second and third SSS reemergence peak is found in the northeastern and southeastern parts of the domain, indicating long cold-season persistence of large-scale SSS anomalies, while only a first SST reemergence is seen. An oceanic reanalysis is used to confirm that the correlation analysis indeed reflects the reemergence of subsurface salinity anomalies.

scholarly journals Application of the ESMO-Magnitude of Clinical Benefit Scale (V.1.1) to the field of early breast cancer therapies

ESMO Open ◽  
2020 ◽  
Vol 5 (5) ◽  
pp. e000743 ◽  
Author(s):  
Shani Paluch-Shimon ◽  
Nathan I Cherny ◽  
Elisabeth G E de Vries ◽  
Urania Dafni ◽  
Martine J Piccart ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Clinical Benefit ◽  
Large Scale ◽  
Disease Free Survival ◽  
Cancer Therapies ◽  
Data Set ◽  
Clinical Scenarios ◽  
Meta Analyses

Click here to listen to the PodcastBackgroundThe European Society for Medical Oncology-Magnitude of Clinical Benefit Scale (ESMO-MCBS) is a validated value scale for solid tumour anticancer treatments. Form 1 of the ESMO-MCBS, used to grade therapies with curative intent including adjuvant therapies, has only been evaluated for a limited number of studies. This is the first large-scale field testing in early breast cancer to assess the applicability of the scale to this data set and the reasonableness of derived scores and to identify any shortcomings to be addressed in future modifications of the scale.MethodRepresentative key studies and meta-analyses of the major modalities of adjuvant systemic therapy of breast cancer were identified for each of the major clinical scenarios (HER2-positive, HER2-negative, endocrine-responsive) and were graded with form 1 of the ESMO-MCBS. These generated scores were reviewed by a panel of experts for reasonableness. Shortcomings and issues related to the application of the scale and interpretation of results were identified and critically evaluated.ResultsSixty-five studies were eligible for evaluation: 59 individual studies and 6 meta-analyses. These studies incorporated 101 therapeutic comparisons, 61 of which were scorable. Review of the generated scores indicated that, with few exceptions, they generally reflected contemporary standards of practice. Six shortcomings were identified related to grading based on disease-free survival (DFS), lack of information regarding acute and long-term toxicity and an inability to grade single-arm de-escalation scales.ConclusionsForm 1 of the ESMO-MCBS is a robust tool for the evaluation of the magnitude of benefit studies in early breast cancer. The scale can be further improved by addressing issues related to grading based on DFS, annotating grades with information regarding acute and long-term toxicity and developing an approach to grade single-arm de-escalation studies.

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