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1. Robustness of AI-based weather forecasts in a changing climate

Author

Rackow, Thomas, Koldunov, Nikolay, Lessig, Christian, Sandu, Irina, Alexe, Mihai, Chantry, Matthew, Clare, Mariana, Dramsch, Jesper, Pappenberger, Florian, Pedruzo-Bagazgoitia, Xabier, Tietsche, Steffen, and Jung, Thomas

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning, Physics - Computational Physics
Abstract

Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the strong links between weather and climate modelling, this raises the question whether machine learning models could also revolutionize climate science, for example by informing mitigation and adaptation to climate change or to generate larger ensembles for more robust uncertainty estimates. Here, we show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states corresponding to pre-industrial, present-day, and future 2.9K warmer climates. This indicates that the dynamics shaping the weather on short timescales may not differ fundamentally in a changing climate. It also demonstrates out-of-distribution generalization capabilities of the machine learning models that are a critical prerequisite for climate applications. Nonetheless, two of the models show a global-mean cold bias in the forecasts for the future warmer climate state, i.e. they drift towards the colder present-day climate they have been trained for. A similar result is obtained for the pre-industrial case where two out of three models show a warming. We discuss possible remedies for these biases and analyze their spatial distribution, revealing complex warming and cooling patterns that are partly related to missing ocean-sea ice and land surface information in the training data. Despite these current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science and transform established approaches by complementing conventional physics-based models., Comment: 14 pages, 4 figures

Published
2024

2. Regional data-driven weather modeling with a global stretched-grid

Author

Nipen, Thomas Nils, Haugen, Håvard Homleid, Ingstad, Magnus Sikora, Nordhagen, Even Marius, Salihi, Aram Farhad Shafiq, Tedesco, Paulina, Seierstad, Ivar Ambjørn, Kristiansen, Jørn, Lang, Simon, Alexe, Mihai, Dramsch, Jesper, Raoult, Baudouin, Mertes, Gert, and Chantry, Matthew

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regional area of interest and maintains a lower resolution elsewhere on the globe. The model is based on graph neural networks, which naturally affords arbitrary multi-resolution grid configurations. The model is applied to short-range weather prediction for the Nordics, producing forecasts at 2.5 km spatial and 6 h temporal resolution. The model is pre-trained on 43 years of global ERA5 data at 31 km resolution and is further refined using 3.3 years of 2.5 km resolution operational analyses from the MetCoOp Ensemble Prediction System (MEPS). The performance of the model is evaluated using surface observations from measurement stations across Norway and is compared to short-range weather forecasts from MEPS. The DDM outperforms both the control run and the ensemble mean of MEPS for 2 m temperature. The model also produces competitive precipitation and wind speed forecasts, but is shown to underestimate extreme events.

Published
2024

3. Data driven weather forecasts trained and initialised directly from observations

Author

McNally, Anthony, Lessig, Christian, Lean, Peter, Boucher, Eulalie, Alexe, Mihai, Pinnington, Ewan, Chantry, Matthew, Lang, Simon, Burrows, Chris, Chrust, Marcin, Pinault, Florian, Villeneuve, Ethel, Bormann, Niels, and Healy, Sean

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses used for training and real-time analyses used for initial conditions are produced by data assimilation, an optimal blending of observations with a physics-based forecast model. As such, many ML forecast systems have an implicit and unquantified dependence on the physics-based models they seek to challenge. Here we propose a new approach, training a neural network to predict future weather purely from historical observations with no dependence on reanalyses. We use raw observations to initialise a model of the atmosphere (in observation space) learned directly from the observations themselves. Forecasts of crucial weather parameters (such as surface temperature and wind) are obtained by predicting weather parameter observations (e.g. SYNOP surface data) at future times and arbitrary locations. We present preliminary results on forecasting observations 12-hours into the future. These already demonstrate successful learning of time evolutions of the physical processes captured in real observations. We argue that this new approach, by staying purely in observation space, avoids many of the challenges of traditional data assimilation, can exploit a wider range of observations and is readily expanded to simultaneous forecasting of the full Earth system (atmosphere, land, ocean and composition).

Published
2024

4. Introduction

Author

Boyer, T, Blunden, J, Dunn, RJH, Ades, Melanie, Adler, Robert, Adusumilli, Susheel, Agyakwah, W, Ahmadpour, Somayeh, Aldeco, Laura S, Alexander, Michael A, Alexe, Mihai, Alfaro, Eric J, Allan, Richard P, Allgood, Adam, Alves, Lincoln M, Amador, Jorge A, Amaya, Dillon J, Amory, Charles, Anderson, John, Andrade, B, Andreassen, Liss Marie, Anneville, Orlane, Aono, Yasuyuki, Arguez, Anthony, Armenteras Pascual, Dolores, Arosio, Carlo, Asher, Elizabeth, Augustine, John A, Avalos, Grinia, Azorin-Molina, Cesar, Baez-Villanueva, Oscar M, Baiman, Rebecca, Ballinger, Thomas J, Banwell, Alison F, Bardin, M Yu, Barichivich, J, Barreira, Sandra, Beadling, Rebecca L, Beauchemin, Marc, Beck, Hylke E, Becker, Emily J, Beckley, Brian, Bekele, E, Bellouin, Nicolas, Benedetti, Angela, Berne, Christine, Berner, Logan T, Bernhard, Germar H, Bhatt, Uma S, Bigalke, Siiri, Bissolli, Peter, Bjerke, Jarle W, Blake, Eric S, Blannin, Josh, Blenkinsop, Stephen, Bochníček, Oliver, Bock, Olivier, Bodin, Xavier, Bonte, Olivier, Bosilovich, Michael G, Boucher, Olivier, Box, Jason E, Bozkurt, Deniz, Brettschneider, Brian, Bringas, Francis G, Brubaker, Mike, Buehler, Stefan A, Bukunt, Brandon, Burgess, David, Butler, Amy H, Byrne, Michael P, Calderón, Blanca, Camargo, Suzana J, Campbell, Jayaka, Campos, Diego, Cappucci, Fabrizio, Carrea, Laura, Carter, Brendan R, Cerveny, Randall, Cetinić, Ivona, Chambers, Don P, Chan, Duo, Chandler, Elise, Chang, Kai-Lan, Charlton, Candice S, Chen, Jack, Chen, Lin, Cheng, Lijing, Cheng, Vincent YS, Chisholm, Lucy, Christiansen, Hanne H, Christy, John R, Chung, Eui-Seok, Ciasto, Laura M, Clarke, Leonardo, Clem, Kyle R, Clingan, Scott, Coelho, Caio AS, Coldewey-Egbers, Melanie, and Colwell, Steve

Subjects
Earth Sciences, Geology, Climate Action, Astronomical and Space Sciences, Atmospheric Sciences, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences, Atmospheric sciences, Climate change science
Abstract

Abstract: —J. Blunden and T. Boyer In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. Anna Glacier in Switzerland and Ice Worm Glacier in the United States disappeared completely. In August, as a direct result of glacial thinning over the past 20 years, a glacial lake on a tributary of the Mendenhall Glacier in Alaska burst through its ice dam and caused unprecedented flooding on Mendenhall River near Juneau. Across the Arctic, the annual surface air temperature was the fourth highest in the 124-year record, and summer (July–September) was record warm. Smaller-than-normal snow cover extent in May and June contributed to the third-highest average peak tundra greenness in the 24-year record. In September, Arctic minimum sea ice extent was the fifth smallest in the 45-year satellite record. The 17 lowest September extents have all occurred in the last 17 years. In Antarctica, temperatures for much of the year were up to 6°C above average over the Weddell Sea and along coastal Dronning Maud Land. The Antarctic Peninsula also experienced well-above-average temperatures during the 2022/23 melt season, which contributed to its fourth consecutive summer of above-average surface melt. On 21 February, Antarctic sea ice extent and sea ice area both reached all-time lows, surpassing records set just a year earlier. Over the course of the year, new daily record-low sea ice extents were set on 278 days. In some instances, these daily records were set by a large margin, for example, the extent on 6 July was 1.8 million km2 lower than the previous record low for that day. Across the global oceans, the annual sea surface temperature was the highest in the 170-year record, far surpassing the previous record of 2016 by 0.13°C. Daily and monthly records were set from March onward, including an historic-high daily global mean sea surface temperature of 18.99°C recorded on 22 August. Approximately 94% of the ocean surface experienced at least one marine heatwave in 2023, while 27% experienced at least one cold spell. Globally averaged ocean heat content from the surface to 2000-m depth was record high in 2023, increasing at a rate equivalent to ∼0.7 Watts per square meter of energy applied over Earth’s surface. Global mean sea level was also record high for the 12th consecutive year, reaching 101.4 mm above the 1993 average when satellite measurements began, an increase of 8.1±1.5 mm over 2022 and the third highest year-over-year increase in the record. A total of 82 named tropical storms were observed during the Northern and Southern Hemispheres’ storm seasons, below the 1991–2020 average of 87. Hurricane Otis became the strongest landfalling hurricane on record for the west coast of Mexico at 140 kt (72 m s−1), causing at least 52 fatalities and $12–16 billion U.S. dollars in damage. Freddy became the world’s longest-lived tropical cyclones on record, developing into a tropical cyclone on 6 February and finally dissipating on 12 March. Freddy crossed the full width of the Indian Ocean and made one landfall in Madagascar and two in Mozambique. In the Mediterranean Sea—outside of traditional tropical cyclone basins—heavy rains and flooding from Storm Daniel killed more than 4300 people and left more than 8000 missing in Libya. The record-warm temperatures in 2023 created conditions that helped intensify the hydrological cycle. Measurements of total-column water vapor in the atmosphere were the highest on record, while the fraction of cloud area in the sky was the lowest since records began in 1980. The annual global mean precipitation total over land surfaces for 2023 was among the lowest since 1979, but global one-day maximum totals were close to average, indicating an increase in rainfall intensity. In July, record-high areas of land across the globe (7.9%) experienced extreme drought, breaking the previous record of 6.2% in July 2022. Overall, 29.7% of land experienced moderate or worse categories of drought during the year, also a record. Mexico reported its driest (and hottest) year since the start of its record in 1950. In alignment with hot and prolonged dry conditions, Canada experienced its worst national wildfire season on record. Approximately 15 million hectares burned across the country, which was more than double the previous record from 1989. Smoke from the fires were transported far into the United States and even to western European countries. August to October 2023 was the driest three-month period in Australia in the 104-year record. Millions of hectares of bushfires burned for weeks in the Northern Territory. In South America, extreme drought developed in the latter half of the year through the Amazon basin. By the end of October, the Rio Negro at Manaus, a major tributary of the Amazon River, fell to its lowest water level since records began in 1902. The transition from La Niña to El Niño helped bring relief to the prolonged drought conditions in equatorial eastern Africa. However, El Niño along with positive Indian Ocean dipole conditions also contributed to excessive rainfall that resulted in devastating floods over southeastern Ethiopia, Somalia, and Kenya during October to December that displaced around 1.5 million people. On 5 September, the town of Zagora, Greece, broke a national record for highest daily rainfall (754 mm in 21 hours, after which the station ceased reporting) due to Storm Daniel; this one-day accumulation was close to Zagora’s normal annual total.

Published
2024

5. Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)

Author

Cai, Shengjuan, Fang, Fangxin, Peuch, Vincent-Henri, Alexe, Mihai, Navon, Ionel Michael, and Wang, Yanghua

Subjects
Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics
Abstract

PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to real-time conditions. Deep learning models show potential in efficiency but still suffer from accuracy loss over time due to error accumulation. To address these challenges, we propose a dual deep neural network (D-DNet) prediction and data assimilation system that efficiently integrates real-time observations, ensuring reliable operational forecasting. D-DNet excels in global operational forecasting for PM2.5 and AOD550, maintaining consistent accuracy throughout the entire year of 2019. It demonstrates notably higher efficiency than the Copernicus Atmosphere Monitoring Service (CAMS) 4D-Var operational forecasting system while maintaining comparable accuracy. This efficiency benefits ensemble forecasting, uncertainty analysis, and large-scale tasks.

Published
2024

6. AIFS -- ECMWF's data-driven forecasting system

Author

Lang, Simon, Alexe, Mihai, Chantry, Matthew, Dramsch, Jesper, Pinault, Florian, Raoult, Baudouin, Clare, Mariana C. A., Lessig, Christian, Maier-Gerber, Michael, Magnusson, Linus, Bouallègue, Zied Ben, Nemesio, Ana Prieto, Dueben, Peter D., Brown, Andrew, Pappenberger, Florian, and Rabier, Florence

Subjects
Physics - Atmospheric and Oceanic Physics
Abstract

Machine learning-based weather forecasting models have quickly emerged as a promising methodology for accurate medium-range global weather forecasting. Here, we introduce the Artificial Intelligence Forecasting System (AIFS), a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS is based on a graph neural network (GNN) encoder and decoder, and a sliding window transformer processor, and is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses. It has a flexible and modular design and supports several levels of parallelism to enable training on high-resolution input data. AIFS forecast skill is assessed by comparing its forecasts to NWP analyses and direct observational data. We show that AIFS produces highly skilled forecasts for upper-air variables, surface weather parameters and tropical cyclone tracks. AIFS is run four times daily alongside ECMWF's physics-based NWP model and forecasts are available to the public under ECMWF's open data policy.

Published
2024

8. Variability and quasi-decadal changes in the methane budget over the period 2000–2012

Author

Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G, Dlugokencky, Edward J, Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N, Castaldi, Simona, Jackson, Robert B, Alexe, Mihai, Arora, Vivek K, Beerling, David J, Bergamaschi, Peter, Blake, Donald R, Brailsford, Gordon, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Covey, Kristofer, Frankenberg, Christian, Gedney, Nicola, Höglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-François, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, Melton, Joe R, Morino, Isamu, Naik, Vaishali, O'Doherty, Simon, Parmentier, Frans-Jan W, Patra, Prabir K, Peng, Changhui, Peng, Shushi, Peters, Glen P, Pison, Isabelle, Prinn, Ronald, Ramonet, Michel, Riley, William J, Saito, Makoto, Santini, Monia, Schroeder, Ronny, Simpson, Isobel J, Spahni, Renato, Takizawa, Atsushi, Thornton, Brett F, Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, Weiss, Ray, Wilton, David J, Wiltshire, Andy, Worthy, Doug, Wunch, Debra, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, and Zhu, Qiuan

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Astronomical and Space Sciences, Meteorology & Atmospheric Sciences, Atmospheric sciences, Climate change science
Abstract

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH4) budget over 2000-2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH4 emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000-2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000-2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008-2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16-32]Tg CH4yr-1 higher methane emissions over the period 2008-2012 compared to 2002-2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002-2006 and 2008-2012 differs from one atmospheric inversion study to another. However, all top-down studies suggest smaller changes in fossil fuel emissions (from oil, gas, and coal industries) compared to the mean of the bottom-up inventories included in this study. This difference is partly driven by a smaller emission change in China from the top-down studies compared to the estimate in the Emission Database for Global Atmospheric Research (EDGARv4.2) inventory, which should be revised to smaller values in a near future. We apply isotopic signatures to the emission changes estimated for individual studies based on five emission sectors and find that for six individual top-down studies (out of eight) the average isotopic signature of the emission changes is not consistent with the observed change in atmospheric 13CH4. However, the partitioning in emission change derived from the ensemble mean is consistent with this isotopic constraint. At the global scale, the top-down ensemble mean suggests that the dominant contribution to the resumed atmospheric CH4 growth after 2006 comes from microbial sources (more from agriculture and waste sectors than from natural wetlands), with an uncertain but smaller contribution from fossil CH4 emissions. In addition, a decrease in biomass burning emissions (in agreement with the biomass burning emission databases) makes the balance of sources consistent with atmospheric 13CH4 observations. In most of the top-down studies included here, OH concentrations are considered constant over the years (seasonal variations but without any inter-annual variability). As a result, the methane loss (in particular through OH oxidation) varies mainly through the change in methane concentrations and not its oxidants. For these reasons, changes in the methane loss could not be properly investigated in this study, although it may play a significant role in the recent atmospheric methane changes as briefly discussed at the end of the paper.

Published
2017

9. Technical note: Evaluation of the Copernicus Atmosphere Monitoring Service Cy48R1 upgrade of June 2023.

Author

Eskes, Henk, Tsikerdekis, Athanasios, Ades, Melanie, Alexe, Mihai, Benedictow, Anna Carlin, Bennouna, Yasmine, Blake, Lewis, Bouarar, Idir, Chabrillat, Simon, Engelen, Richard, Errera, Quentin, Flemming, Johannes, Garrigues, Sebastien, Griesfeller, Jan, Huijnen, Vincent, Ilić, Luka, Inness, Antje, Kapsomenakis, John, Kipling, Zak, and Langerock, Bavo

Subjects
STRATOSPHERIC chemistry, ATMOSPHERIC chemistry, FOURIER transform spectrometers, NUMERICAL weather forecasting, POLLUTION measurement, TRACE gases
Abstract

The Copernicus Atmosphere Monitoring Service (CAMS) provides daily analyses and forecasts of the composition of the atmosphere, including the reactive gases such as O3 , CO , NO2 , HCHO and SO2 ; aerosol species; and greenhouse gases. The global CAMS analysis system (IFS-COMPO) is based on the ECMWF Integrated Forecasting System (IFS) for numerical weather prediction (NWP) and assimilates a large number of composition satellite products on top of the meteorological observations ingested in IFS. The CAMS system receives regular upgrades, following the upgrades of IFS. The last upgrade, Cy48R1, operational since 27 June 2023, was major with a large number of code changes, both for IFS-COMPO and for NWP. The main IFS-COMPO innovations include the introduction of full stratospheric chemistry; a major update of the emissions; a major update of the aerosol model, including the representation of secondary organic aerosol; several updates of the dust life cycle and optics; updates to the inorganic chemistry in the troposphere; and the assimilation of Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol optical depth (AOD) and TROPOspheric Monitoring Instrument (TROPOMI) CO. The CAMS Cy48R1 upgrade was validated using a large number of independent measurement datasets, including surface in situ, surface remote sensing, routine aircraft, and balloon and satellite observations. In this paper we present the validation results for Cy48R1 by comparing them with the skill of the previous operational system (Cy47R3), with the independent observations as reference, for the period October 2022 to June 2023, during which daily forecasts from both cycles are available. Major improvements in skill are found for the ozone profile in the lower–middle stratosphere and for stratospheric NO2 due to the inclusion of full stratospheric chemistry. Stratospheric trace gases compare well with the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) observations between 10 and 200 hPa, with larger deviations between 1 and 10 hPa. The impact of the updated emissions is especially visible over East Asia and is beneficial for the trace gases O3 , NO2 and SO2. The CO column assimilation is now anchored by the Infrared Atmospheric Sounding Interferometer (IASI) instead of the Measurements Of Pollution in The Troposphere (MOPITT) instrument, which is beneficial for most of the CO comparisons, and the assimilation of TROPOMI CO data improves the model CO field in the troposphere. In general the aerosol optical depth has improved globally, but the dust evaluation shows more mixed results. The results of the 47 comparisons are summarised in a scorecard, which shows that 83 % of the evaluation datasets show a neutral or improved performance of Cy48R1 compared to the previous operational CAMS system, while 17 % indicate a (slight) degradation. This demonstrates the overall success of this upgrade. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Technical Note: Evaluation of the Copernicus Atmosphere Monitoring Service Cy48R1 upgrade of June 2023

Author

Eskes, Henk, primary, Tsikerdekis, Athanasios, additional, Ades, Melanie, additional, Alexe, Mihai, additional, Benedictow, Anna Carlin, additional, Bennouna, Yasmine, additional, Blake, Lewis, additional, Bouarar, Idir, additional, Chabrillat, Simon, additional, Engelen, Richard, additional, Errera, Quentin, additional, Flemming, Johannes, additional, Garrigues, Sebastien, additional, Griesfeller, Jan, additional, Huijnen, Vincent, additional, Ilic, Luka, additional, Inness, Antje, additional, Kapsomenakis, John, additional, Kipling, Zak, additional, Langerock, Bavo, additional, Mortier, Augustin, additional, Parrington, Mark, additional, Pison, Isabelle, additional, Pitkanen, Mikko, additional, Remy, Samuel, additional, Richter, Andreas, additional, Schoenhardt, Anja, additional, Schulz, Michael, additional, Thouret, Valerie, additional, Warneke, Thorsten, additional, Zerefos, Christos, additional, and Peuch, Vincent-Henri, additional

Published
2024
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11. The Global Methane Budget: 2000–2012

Author

Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G, Dlugokencky, Edward J, Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N, Castaldi, Simona, Jackson, Robert B, Alexe, Mihai, Arora, Vivek K, Beerling, David J, Bergamaschi, Peter, Blake, Donald R, Brailsford, Gordon, Brovkin, Victor, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Curry, Charles, Frankenberg, Christian, Gedney, Nicola, Höglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-François, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, McDonald, Kyle C, Marshall, Julia, Melton, Joe R, Morino, Isamu, O'Doherty, Simon, Parmentier, Frans-Jan W, Patra, Prabir K, Peng, Changhui, Peng, Shushi, Peters, Glen P, Pison, Isabelle, Prigent, Catherine, Prinn, Ronald, Ramonet, Michel, Riley, William J, Saito, Makoto, Schroeder, Ronny, Simpson, Isobel J, Spahni, Renato, Steele, Paul, Takizawa, Atsushi, Thornton, Brett F, Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, van Weele, Michiel, van der Werf, Guido, Weiss, Ray, Wiedinmyer, Christine, Wilton, David J, Wiltshire, Andy, Worthy, Doug, Wunch, Debra B, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, and Zhu, Qiuan

Abstract

Abstract. The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (~biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (T-D, exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories, and data-driven approaches (B-U, including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003–2012 decade, global methane emissions are estimated by T-D inversions at 558 Tg CH4 yr−1 (range [540–568]). About 60 % of global emissions are anthropogenic (range [50–65 %]). B-U approaches suggest larger global emissions (736 Tg CH4 yr−1 [596–884]) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the T-D budget, it is likely that some of the individual emissions reported by the B-U approaches are overestimated, leading to too large global emissions. Latitudinal data from T-D emissions indicate a predominance of tropical emissions (~64 % of the global budget,

Published
2016

12. Introduction

Author

Boyer, Tim, primary, Bartow-Gillies, Ellen, additional, Abida, A., additional, Ades, Melanie, additional, Adler, Robert, additional, Adusumilli, Susheel, additional, Agyakwah, W., additional, Ahmasuk, Brandon, additional, Aldeco, Laura S., additional, Alexe, Mihai, additional, Alfaro, Eric J., additional, Allan, Richard P., additional, Allgood, Adam, additional, Alves, Lincoln. M., additional, Amador, Jorge A., additional, Anderson, John, additional, Andrade, B., additional, Anneville, Orlane, additional, Aono, Yasuyuki, additional, Arguez, Anthony, additional, Arosio, Carlo, additional, Atkinson, C., additional, Augustine, John A., additional, Avalos, Grinia, additional, Azorin-Molina, Cesar, additional, Backensto, Stacia A., additional, Bader, Stephan, additional, Baez, Julian, additional, Baiman, Rebecca, additional, Ballinger, Thomas J., additional, Banwell, Alison F., additional, Bardin, M. Yu, additional, Barichivich, Jonathan, additional, Barnes, John E., additional, Barreira, Sandra, additional, Beadling, Rebecca L., additional, Beck, Hylke E., additional, Becker, Emily J., additional, Bekele, E., additional, Bellido, Guillem Martín, additional, Bellouin, Nicolas, additional, Benedetti, Angela, additional, Benestad, Rasmus, additional, Berne, Christine, additional, Berner, Logan. T., additional, Bernhard, Germar H., additional, Bhatt, Uma S., additional, Bhuiyan, A. E., additional, Bigalke, Siiri, additional, Biló, Tiago, additional, Bissolli, Peter, additional, Bjerke Jarle, W., additional, Blagrave, Kevin, additional, Blake, Eric S., additional, Blenkinsop, Stephen, additional, Blunden, Jessica, additional, Bochníček, Oliver, additional, Bock, Olivier, additional, Bodenstein, Barbara, additional, Bodin, Xavier, additional, Bosilovich, Michael, additional, Boucher, Olivier, additional, Bozkurt, Deniz, additional, Brettschneider, Brian, additional, Bringas, Francis G., additional, Bringas, Francis, additional, Buechler, Dennis, additional, Buehler, Stefan A., additional, Bukunt, Brandon, additional, Calderón, Blanca, additional, Camargo, Suzana J., additional, Campbell, Jayaka, additional, Campos, Diego, additional, Carrea, Laura, additional, Carter, Brendan R., additional, Cetinić, Ivona, additional, Chambers, Don P., additional, Chan, Duo, additional, Chandler, Elise, additional, Chang, Kai-Lan, additional, Chen, Hua, additional, Chen, Lin, additional, Cheng, Lijing, additional, Cheng, Vincent Y. S., additional, Chomiak, Leah, additional, Christiansen, Hanne H., additional, Christy, John R., additional, Chung, Eui-Seok, additional, Ciasto, Laura M., additional, Clarke, Leonardo, additional, Clem, Kyle R., additional, Clingan, Scott, additional, Coelho, Caio A.S., additional, Cohen, Judah L., additional, Coldewey-Egbers, Melanie, additional, Colwell, Steve, additional, Cooper, Owen R., additional, Cornes, Richard C., additional, Correa, Kris, additional, Costa, Felipe, additional, Covey, Curt, additional, Coy, Lawrence, additional, Créatux, Jean-François, additional, Crhova, Lenka, additional, Crimmins, Theresa, additional, Cronin, Meghan F., additional, Cropper, Thomas, additional, Crotwell, Molly, additional, Culpepper, Joshua, additional, Cunha, Ana P., additional, Cusicanqui, Diego, additional, Datta, Rajashree T., additional, Davis, Sean M., additional, De Bock, Veerle, additional, de Jeu, Richard A. M., additional, De Laat, Jos, additional, Decharme, Bertrand, additional, Degenstein, Doug, additional, Delaloye, Reynald, additional, Demircan, Mesut, additional, Derksen, Chris, additional, Deus, Ricardo, additional, Dhurmea, K. R., additional, Diamond, Howard J., additional, Dirkse, S., additional, Divine, Dmitry, additional, Dokulil, Martin T., additional, Donat, Markus G., additional, Dong, Shenfu, additional, Dorigo, Wouter A., additional, Drost Jensen, Caroline, additional, Druckenmiller, Matthew L., additional, Drumond, Paula, additional, du Plessis, Marcel, additional, Dugan, Hilary A., additional, Dulamsuren, Dashkhuu, additional, Dunmire, Devon, additional, Dunn, Robert J. H., additional, Durre, Imke, additional, Dusek, Robert, additional, Dutton, Geoff, additional, Duveiller, Gregory, additional, Ekici, Mithat, additional, Elias Chereque, Alesksandra, additional, ElKharrim, M., additional, Epstein, Howard E., additional, Espinoza, Jhan-Carlo, additional, Estilow, Thomas W., additional, Estrella, Nicole, additional, Fauchereau, Nicolas, additional, Fausto, Robert S., additional, Feely, Richard A., additional, Fenimore, Chris, additional, Fereday, David, additional, Fettweis, Xavier, additional, Fioletov, vitali E., additional, Flemming, Johannes, additional, Fogarty, Chris, additional, Fogt, Ryan L., additional, Forbes, Bruce C., additional, Foster, Michael J., additional, Franz, Bryan A., additional, Freeman, Natalie M., additional, Fricker, Helen A., additional, Frith, Stacey M., additional, Froidevaux, Lucien, additional, Frost, Gerald V. (JJ), additional, Fuhrman, Steven, additional, Füllekrug, Martin, additional, Ganter, Catherine, additional, Gao, Meng, additional, Gardner, Alex S., additional, Garforth, Judith, additional, Garg, Jay, additional, Gerland, Sebastian, additional, Gibbes, Badin, additional, Gille, Sarah T., additional, Gilson, John, additional, Gleason, Karin, additional, Gobron, Nadine, additional, Goetz, Scott J., additional, Goldenberg, Stanley B., additional, Goni, Gustavo, additional, Goodman, Steven, additional, Goto, Atsushi, additional, Grooß, Jens-Uwe, additional, Gruber, Alexander, additional, Gu, Guojun, additional, Guard, Charles “Chip” P., additional, Hagos, S., additional, Hahn, Sebastian, additional, Haimberger, Leopold, additional, Hall, Bradley D., additional, Hamlington, Benjamin D., additional, Hanna, Edward, additional, Hanssen-Bauer, Inger, additional, Harnos, Daniel S., additional, Harris, Ian, additional, He, Qiong, additional, Heim, Richard R., additional, Hellström, Sverker, additional, Hemming, Deborah L., additional, Hendricks, Stefan, additional, Hicks, J., additional, Hidalgo, Hugo G., additional, Hirschi, Martin, additional, Ho, Shu-peng (Ben), additional, Hobbs, W., additional, Holmes, Robert M., additional, Holzworth, Robert, additional, Hrbáček, Filip, additional, Hu, Guojie, additional, Hu, Zeng-Zhen, additional, Huang, Boyin, additional, Huang, Hongjie, additional, Hurst, Dale F., additional, Ialongo, Iolanda, additional, Inness, Antje, additional, Isaksen, Ketil, additional, Ishii, Masayoshi, additional, Jadra, Gerardo, additional, Jevrejeva, Svetlana, additional, John, Viju O., additional, Johns, W., additional, Johnsen, Bjørn, additional, Johnson, Bryan, additional, Johnson, Gregory C., additional, Jones, Philip D., additional, Jones, Timothy, additional, Josey, Simon A., additional, Jumaux, G., additional, Junod, Robert, additional, Kääb, Andreas, additional, Kabidi, K., additional, Kaiser, Johannes W., additional, Kaler, Robb S.A., additional, Kaleschke, Lars, additional, Kaufmann, Viktor, additional, Kazemi, Amin Fazl, additional, Keller, Linda M., additional, Kellerer-Pirklbauer, Andreas, additional, Kendon, Mike, additional, Kennedy, John, additional, Kent, Elizabeth C., additional, Kerr, Kenneth, additional, Khan, Valentina, additional, Khiem, Mai Van, additional, Kidd, Richard, additional, Kim, Mi Ju, additional, Kim, Seong-Joong, additional, Kipling, Zak, additional, Klotzbach, Philip J., additional, Knaff, John A., additional, Koppa, Akash, additional, Korshunova, Natalia N., additional, Kraemer, Benjamin M., additional, Kramarova, Natalya A., additional, Kruger, A. C., additional, Kruger, Andries, additional, Kumar, Arun, additional, L’Heureux, Michelle, additional, La Fuente, Sofia, additional, Laas, Alo, additional, Labe, Zachary M., additional, Lader, Rick, additional, Lakatos, Mónika, additional, Lakkala, Kaisa, additional, Lam, Hoang Phuc, additional, Lan, Xin, additional, Landschützer, Peter, additional, Landsea, Chris W., additional, Lang, Timothy, additional, Lankhorst, Matthias, additional, Lantz, Kathleen O., additional, Lara, Mark J., additional, Lavado-Casimiro, Waldo, additional, Lavers, David A., additional, Lazzara, Matthew A., additional, Leblanc, Thierry, additional, Lee, Tsz-Cheung, additional, Leibensperger, Eric M., additional, Lennard, Chris, additional, Leuliette, Eric, additional, Leung, Kinson H. Y., additional, Lieser, Jan L., additional, Likso, Tanja, additional, Lin, I-I., additional, Lindsey, Jackie, additional, Liu, Yakun, additional, Locarnini, Ricardo, additional, Loeb, Norman G., additional, Loomis, Bryant D., additional, Lorrey, Andrew M., additional, Loyola, Diego, additional, Lu, Rui, additional, Lumpkin, Rick, additional, Luo, Jing-Jia, additional, Luojus, Kari, additional, Lyman, John M., additional, Maberly, Stephen C., additional, Macander, Matthew J., additional, MacFerrin, Michael, additional, MacGilchrist, Graeme A., additional, MacLennan, Michelle L., additional, Madelon, Remi, additional, Magee, Andrew D., additional, Magnin, Florence, additional, Mamen, Jostein, additional, Mankoff, Ken D., additional, Manney, Gloria L., additional, Marcinonienė, Izolda, additional, Marengo, Jose A., additional, Marjan, Mohammadi, additional, Martínez, Ana E., additional, Massom, Robert A., additional, Matsuzaki, Shin-Ichiro, additional, May, Linda, additional, Mayer, Michael, additional, Mazloff, Matthew R., additional, McAfee, Stephanie A., additional, McBride, C., additional, McCabe, Matthew F., additional, McClelland, James W., additional, McPhaden, Michael J., additional, Mcvicar, Tim R., additional, Mears, Carl A., additional, Meier, Walter N., additional, Mekonnen, A., additional, Menzel, Annette, additional, Merchant, Christopher J., additional, Merrifield, Mark A., additional, Meyer, Michael F., additional, Meyers, Tristan, additional, Mikolajczyk, David E., additional, Miller, John B., additional, Miralles, Diego G., additional, Misevicius, Noelia, additional, Mishonov, Alexey, additional, Mitchum, Gary T., additional, Moat, Ben I., additional, Moesinger, Leander, additional, Moise, Aurel, additional, Molina-Carpio, Jorge, additional, Monet, Ghislaine, additional, Montzka, Stephan A., additional, Moon, Twila A., additional, Moore, G. W. K., additional, Mora, Natali, additional, Morán, Johnny, additional, Morehen, Claire, additional, Morice, Colin, additional, Mostafa, A. E., additional, Mote, Thomas L., additional, Mrekaj, Ivan, additional, Mudryk, Lawrence, additional, Mühle, Jens, additional, Müller, Rolf, additional, Nance, David, additional, Nash, Eric R., additional, Nerem, R. Steven, additional, Newman, Paul A., additional, Nicolas, Julien P., additional, Nieto, Juan J., additional, Noetzli, Jeannette, additional, Noll, Ben, additional, Norton, Taylor, additional, Nyland, Kelsey E., additional, O’Keefe, John, additional, Ochwat, Naomi, additional, Oikawa, Yoshinori, additional, Okunaka, Yuka, additional, Osborn, Timothy J., additional, Overland, James E., additional, Park, Taejin, additional, Parrington, Mark, additional, Parrish, Julia K., additional, Pasch, Richard J., additional, Pascual Ramírez, Reynaldo, additional, Pellet, Cécile, additional, Pelto, Mauri S., additional, Perčec Tadić, Melita, additional, Perovich, Donald K., additional, Petersen, Guðrún Nína, additional, Petersen, Kyle, additional, Petropavlovskikh, Irina, additional, Petty, Alek, additional, Pezza, Alexandre B., additional, Pezzi, Luciano P., additional, Phillips, Coda, additional, Phoenix, Gareth K., additional, Pierson, Don, additional, Pinto, Izidine, additional, Pires, Vanda, additional, Pitts, Michael, additional, Po-Chedley, Stephen, additional, Pogliotti, Paolo, additional, Poinar, Kristin, additional, Polvani, Lorenzo, additional, Preimesberger, Wolfgang, additional, Price, Colin, additional, Pulkkanen, Merja, additional, Purkey, Sarah G., additional, Qiu, Bo, additional, Quisbert, Kenny, additional, Quispe, Willy R., additional, Rajeevan, M., additional, Ramos, Andrea M., additional, Randel, William J., additional, Rantanen, Mika, additional, Raphael, Marilyn N., additional, Reagan, James, additional, Recalde, Cristina, additional, Reid, Phillip, additional, Rémy, Samuel, additional, Reyes Kohler, Alejandra J., additional, Ricciardulli, Lucrezia, additional, Richardson, Andrew D., additional, Ricker, Robert, additional, Robinson, David A., additional, Robjhon, M., additional, Rocha, Willy, additional, Rodell, Matthew, additional, Rodriguez Guisado, Esteban, additional, Rodriguez-Fernandez, Nemesio, additional, Romanovsky, Vladimir E., additional, Ronchail, Josyane, additional, Rosencrans, Matthew, additional, Rosenlof, Karen H., additional, Rösner, Benjamin, additional, Rösner, Henrieke, additional, Rozanov, Alexei, additional, Rozkošný, Jozef, additional, Rubek, Frans, additional, Rusanovskaya, Olga O., additional, Rutishauser, This, additional, Sabeerali, C. T., additional, Salinas, Roberto, additional, Sánchez-Lugo, Ahira, additional, Santee, Michelle L., additional, Santini, Marcelo, additional, Sato, Katsunari, additional, Sawaengphokhai, Parnchai, additional, Sayouri, A., additional, Scambos, Theodore, additional, Schenzinger, Verena, additional, Schimanke, Semjon, additional, Schlegel, Robert W., additional, Schmid, Claudia, additional, Schmid, Martin, additional, Schneider, Udo, additional, Schreck, Carl J., additional, Schultz, Cristina, additional, _, _, additional, Segele, Z. T., additional, Sensoy, Serhat, additional, Serbin, Shawn P., additional, Serreze, Mark C., additional, Setiawan, Amsari Mudzakir, additional, Sezaki, Fumi, additional, Sharma, Sapna, additional, Sharp, Jonathan D., additional, Sheffield, Gay, additional, Shi, Jia-Rui, additional, Shi, Lei, additional, Shiklomanov, Alexander I., additional, Shiklomanov, Nikolay I., additional, Shimaraeva, Svetlana V., additional, Shukla, R., additional, Siegel, David A., additional, Silow, Eugene A., additional, Sima, F., additional, Simmons, Adrian J., additional, Smeed, David A., additional, Smith, Adam, additional, Smith, Matthew M., additional, Smith, Sharon L., additional, Soden, Brian J., additional, Sofieva, Viktoria, additional, Souza, Everaldo, additional, Sparks, Tim H., additional, Spence-Hemmings, Jacqueline, additional, Spencer, Robert G. M., additional, Spillane, Sandra, additional, Sreejith, O. P., additional, Srivastava, A. K., additional, Stackhouse, Paul W., additional, Stammerjohn, Sharon, additional, Stauffer, Ryan, additional, Steinbrecht, Wolfgang, additional, Steiner, Andrea K., additional, Stella, Jose L., additional, Stephenson, Tannecia S., additional, Stradiotti, Pietro, additional, Strahan, Susan E., additional, Streletskiy, Dmitry A., additional, Surendran, Divya E., additional, Suslova, Anya, additional, Svendby, Tove, additional, Sweet, William, additional, Takahashi, Kiyotoshi, additional, Takemura, Kazuto, additional, Tank, Suzanne E., additional, Taylor, Michael A., additional, Tedesco, Marco, additional, Thackeray, Stephen J., additional, Thiaw, W. M., additional, Thibert, Emmanuel, additional, Thoman, Richard L., additional, Thompson, Andrew F., additional, Thompson, Philip R., additional, Tian-Kunze, Xiangshan, additional, Timmermans, Mary-Louise, additional, Timofeyev, Maxim A., additional, Tobin, Skie, additional, Tømmervik, Hans, additional, Tourpali, Kleareti, additional, Trescilo, Lidia, additional, Tretiakov, Mikhail, additional, Trewin, Blair C., additional, Triñanes, Joaquin A., additional, Trotman, Adrian, additional, Truchelut, Ryan E., additional, Trusel, Luke D., additional, Tye, Mari R., additional, van der A, Ronald, additional, van der Schalie, Robin, additional, van der Schrier, Gerard, additional, Van Hemert, Caroline, additional, Van Meerbeeck, Cedric J., additional, van vliet, Arnold J.H., additional, Vazife, Ahad, additional, Verburg, Piet, additional, Vernier, Jean-Paul, additional, Vimont, Isaac J., additional, Virts, Katrina, additional, Vivero, Sebastián, additional, Volkov, Denis L., additional, Vömel, Holger, additional, Vose, Russell S., additional, Walker, Donald (Skip) A., additional, Walsh, John E., additional, Wang, Bin, additional, Wang, Hui, additional, Wang, Muyin, additional, Wang, Ray H. J., additional, Wang, Xinyue, additional, Wanninkhof, Rik, additional, Warnock, Taran, additional, Weber, Mark, additional, Webster, Melinda, additional, Wehrlé, Adrian, additional, Wen, Caihong, additional, Westberry, Toby K., additional, Widlansky, Matthew J., additional, Wiese, David N., additional, Wild, Jeannette D., additional, Wille, Jonathan D., additional, Willems, An, additional, Willett, Kate M., additional, Williams, Earle, additional, Willis, J., additional, Wong, Takmeng, additional, Wood, Kimberly M., additional, Woolway, Richard Iestyn, additional, Xie, Ping-Ping, additional, Yang, Daqing, additional, Yin, Xungang, additional, Yin, Ziqi, additional, Zeng, Zhenzhong, additional, Zhang, Huai-min, additional, Zhang, Li, additional, Zhang, Peiqun, additional, Zhao, Lin, additional, Zhou, Xinjia, additional, Zhu, Zhiwei, additional, Ziemke, Jerry R., additional, Ziese, Markus, additional, Zolkos, Scott, additional, Zotta, Ruxandra M., additional, Zou, Cheng-Zhi, additional, Allen, Jessicca, additional, Camper, Amy V., additional, Haley, Bridgette O., additional, Hammer, Gregory, additional, Love-Brotak, S. Elizabeth, additional, Ohlmann, Laura, additional, Noguchi, Lukas, additional, Riddle, Deborah B., additional, and Veasey, Sara W., additional

Published
2023
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13. Global Climate

Author

Dunn, Robert J. H., primary, Miller, John B., additional, Willett, Kate M., additional, Gobron, Nadine, additional, Ades, Melanie, additional, Adler, Robert, additional, Alexe, Mihai, additional, Allan, Richard P., additional, Anderson, John, additional, Anneville, Orlane, additional, Aono, Yasuyuki, additional, Arguez, Anthony, additional, Arosio, Carlo, additional, Augustine, John A., additional, Azorin-Molina, Cesar, additional, Barichivich, Jonathan, additional, Barnes, John E., additional, Beck, Hylke E., additional, Bellouin, Nicolas, additional, Benedetti, Angela, additional, Blagrave, Kevin, additional, Blenkinsop, Stephen, additional, Bock, Olivier, additional, Bodin, Xavier, additional, Bosilovich, Michael, additional, Boucher, Olivier, additional, Buechler, Dennis, additional, Buehler, Stefan A., additional, Campos, Diego, additional, Carrea, Laura, additional, Chang, Kai-Lan, additional, Christiansen, Hanne H., additional, Christy, John R., additional, Chung, Eui-Seok, additional, Ciasto, Laura M., additional, Clingan, Scott, additional, Coldewey-Egbers, Melanie, additional, Cooper, Owen R., additional, Cornes, Richard C., additional, Covey, Curt, additional, Créatux, Jean-François, additional, Crimmins, Theresa, additional, Cropper, Thomas, additional, Crotwell, Molly, additional, Culpepper, Joshua, additional, Cusicanqui, Diego, additional, Davis, Sean M., additional, de Jeu, Richard A. M., additional, Degenstein, Doug, additional, Delaloye, Reynald, additional, Dokulil, Martin T., additional, Donat, Markus G., additional, Dorigo, Wouter A., additional, Dugan, Hilary A., additional, Durre, Imke, additional, Dutton, Geoff, additional, Duveiller, Gregory, additional, Estilow, Thomas W., additional, Estrella, Nicole, additional, Fereday, David, additional, Fioletov, Vitali E., additional, Flemming, Johannes, additional, Foster, Michael J., additional, Franz, Bryan, additional, Frith, Stacey M., additional, Froidevaux, Lucien, additional, Füllekrug, Martin, additional, Garforth, Judith, additional, Garg, Jay, additional, Gibbes, Badin, additional, Goodman, Steven, additional, Goto, Atsushi, additional, Gruber, Alexander, additional, Gu, Guojun, additional, Hahn, Sebastian, additional, Haimberger, Leopold, additional, Hall, Bradley D., additional, Harris, Ian, additional, Hemming, Deborah L., additional, Hirschi, Martin, additional, Ho, Shu-peng, additional, Holzworth, Robert, additional, Hrbáček, Filip, additional, Hu, Guojie, additional, Hurst, Dale F., additional, Inness, Antje, additional, Isaksen, Ketil, additional, John, Viju O., additional, Jones, Philip D., additional, Junod, Robert, additional, Kääb, Andreas, additional, Kaiser, Johannes W., additional, Kaufmann, Viktor, additional, Kellerer-Pirklbauer, Andreas, additional, Kent, Elizabeth C., additional, Kidd, Richard, additional, Kipling, Zak, additional, Koppa, Akash, additional, Kraemer, Benjamin M., additional, Kramarova, Natalya, additional, Kruger, Andries, additional, La Fuente, Sofia, additional, Laas, Alo, additional, Lan, Xin, additional, Lang, Timothy, additional, Lantz, Kathleen O., additional, Lavers, David A., additional, Leblanc, Thierry, additional, Leibensperger, Eric M., additional, Lennard, Chris, additional, Liu, Yakun, additional, Loeb, Norman G., additional, Loyola, Diego, additional, Maberly, Stephen C., additional, Madelon, Remi, additional, Magnin, Florence, additional, Matsuzaki, Shin-Ichiro, additional, May, Linda, additional, Mayer, Michael, additional, McCabe, Matthew F., additional, McVicar, Tim R., additional, Mears, Carl A., additional, Menzel, Annette, additional, Merchant, Christopher J., additional, Meyer, Michael F., additional, Miralles, Diego G., additional, Moesinger, Leander, additional, Monet, Ghislaine, additional, Montzka, Stephan A., additional, Morice, Colin, additional, Mrekaj, Ivan, additional, Mühle, Jens, additional, Nance, David, additional, Nicolas, Julien P., additional, Noetzli, Jeannette, additional, Noll, Ben, additional, O’Keefe, John, additional, Osborn, Timothy J., additional, Park, Taejin, additional, Parrington, Mark, additional, Pellet, Cécile, additional, Pelto, Mauri S., additional, Petersen, Kyle, additional, Phillips, Coda, additional, Pierson, Don, additional, Pinto, Izidine, additional, Po-Chedley, Stephen, additional, Pogliotti, Paolo, additional, Polvani, Lorenzo, additional, Preimesberger, Wolfgang, additional, Price, Colin, additional, Pulkkanen, Merja, additional, Randel, William J., additional, Rémy, Samuel, additional, Ricciardulli, Lucrezia, additional, Richardson, Andrew D., additional, Robinson, David A., additional, Rocha, Willy, additional, Rodell, Matthew, additional, Rodriguez-Fernandez, Nemesio, additional, Rosenlof, Karen H., additional, Rozanov, Alexei, additional, Rozkošný, Jozef, additional, Rusanovskaya, Olga O., additional, Rutishauser, This, additional, Sabeerali, C. T., additional, Sánchez-Lugo, Ahira, additional, Sawaengphokhai, Parnchai, additional, Schenzinger, Verena, additional, Schlegel, Robert W., additional, Schmid, Martin, additional, Schneider, Udo, additional, Sezaki, Fumi, additional, Sharma, Sapna, additional, Shi, Lei, additional, Shimaraeva, Svetlana V., additional, Silow, Eugene A., additional, Simmons, Adrian J., additional, Smith, Sharon L., additional, Soden, Brian J., additional, Sofieva, Viktoria, additional, Sparks, Tim H., additional, Sreejith, O.P., additional, Stackhouse, Paul W., additional, Stauffer, Ryan, additional, Steinbrecht, Wolfgang, additional, Steiner, Andrea K., additional, Stradiotti, Pietro, additional, Streletskiy, Dmitry A., additional, Surendran, Divya E., additional, Thackeray, Stephen J., additional, Thibert, Emmanuel, additional, Timofeyev, Maxim A., additional, Tourpali, Kleareti, additional, Tye, Mari R., additional, van der A, Ronald, additional, van der Schalie, Robin, additional, van der Schrier, Gerard, additional, van Vliet, Arnold J.H., additional, Verburg, Piet, additional, Vernier, Jean-Paul, additional, Vimont, Isaac J., additional, Virts, Katrina, additional, Vivero, Sebastián, additional, Vömel, Holger, additional, Vose, Russell S., additional, Wang, Ray H. J., additional, Wang, Xinyue, additional, Warnock, Taran, additional, Weber, Mark, additional, Wiese, David N., additional, Wild, Jeannette D., additional, Williams, Earle, additional, Wong, Takmeng, additional, Woolway, Richard Iestyn, additional, Yin, Xungang, additional, Zeng, Zhenzhong, additional, Zhao, Lin, additional, Zhou, Xinjia, additional, Ziemke, Jerry R., additional, Ziese, Markus, additional, Zotta, Ruxandra M., additional, Zou, Cheng-Zhi, additional, Allen, Jessicca, additional, Camper, Amy V., additional, Haley, Bridgette O., additional, Hammer, Gregory, additional, Love-Brotak, S. Elizabeth, additional, Ohlmann, Laura, additional, Noguchi, Lukas, additional, Riddle, Deborah B., additional, and Veasey, Sara W., additional

Published
2023
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14. Bridging the European Earth-Observation and AI Communities for Data-Intensive Innovation

Author

Troumpoukis, Antonis, primary, Klampanos, Iraklis, additional, Pantazi, Despina-Athanasia, additional, Tsalapati, Eleni, additional, Albughdadi, Mohanad, additional, Alexe, Mihai, additional, Baousis, Vasileios, additional, Barrilero, Omar, additional, Billière, Bryce, additional, Bojor, Alexandra, additional, Branco, Pedro, additional, Bruzzone, Lorenzo, additional, Chietera, Andreina, additional, Fournand, Philippe, additional, Hall, Richard, additional, Hassan, David, additional, Lazzarini, Michele, additional, Luna, Adrian, additional, Punjani, Dharmen, additional, Stamoulis, George, additional, Weikmann, Giulio, additional, Ziółkowski, Marcin, additional, Ziouvelou, Xenia, additional, Koubarakis, Manolis, additional, and Karkaletsis, Vangelis, additional

Published
2023
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15. Introduction STATE OF THE CLIMATE IN 2022

Author

Boyer, Tim, Bartow-Gillies, Ellen, Abida, A, Ades, Melanie, Adler, Robert, Adusumilli, Susheel, Agyakwah, W, Ahmasuk, Brandon, Aldeco, Laura S, Alexe, Mihai, Alfaro, Eric J, Allan, Richard P, Allgood, Adam, Alves, Lincoln M, Amador, Jorge A, Anderson, John, Andrade, B, Anneville, Orlane, Aono, Yasuyuki, Arguez, Anthony, Arosio, Carlo, Atkinson, C, Augustine, John A, Avalos, Grinia, Azorin-Molina, Cesar, Backensto, Stacia A, Bader, Stephan, Baez, Julian, Baiman, Rebecca, Ballinger, Thomas J, Banwell, Alison F, Bardin, M Yu, Barichivich, Jonathan, Barnes, John E, Barreira, Sandra, Beadling, Rebecca L, Beck, Hylke E, Becker, Emily J, Bekele, E, Bellido, Guillem Martín, Bellouin, Nicolas, Benedetti, Angela, Benestad, Rasmus, Berne, Christine, Berner, Logan T, Bernhard, Germar H, Bhatt, Uma S, Bhuiyan, AE, Bigalke, Siiri, Biló, Tiago, Bissolli, Peter, Bjerke Jarle, W, Blagrave, Kevin, Blake, Eric S, Blenkinsop, Stephen, Blunden, Jessica, Bochníček, Oliver, Bock, Olivier, Bodin, Xavier, Bosilovich, Michael, Boucher, Olivier, Bozkurt, Deniz, Brettschneider, Brian, Bringas, Francis G, Bringas, Francis, Buechler, Dennis, Buehler, Stefan A, Bukunt, Brandon, Calderón, Blanca, Camargo, Suzana J, Campbell, Jayaka, Campos, Diego, Carrea, Laura, Carter, Brendan R, Cetinić, Ivona, Chambers, Don P, Chan, Duo, Chandler, Elise, Chang, Kai-Lan, Chen, Hua, Chen, Lin, Cheng, Lijing, Cheng, Vincent YS, Chomiak, Leah, Christiansen, Hanne H, Christy, John R, Chung, Eui-Seok, Ciasto, Laura M, Clarke, Leonardo, Clem, Kyle R, Clingan, Scott, Coelho, Caio AS, Cohen, Judah L, Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R, Cornes, Richard C, Correa, Kris, Costa, Felipe, Covey, Curt, Coy, Lawrence, Créatux, Jean-François, Crhova, Lenka, Crimmins, Theresa, Cronin, Meghan F, Cropper, Thomas, Crotwell, Molly, Culpepper, Joshua, Cunha, Ana P, Cusicanqui, Diego, Datta, Rajashree T, Davis, Sean M, De Bock, Veerle, de Jeu, Richard AM, De Laat, Jos, Decharme, Bertrand, Degenstein, Doug, Delaloye, Reynald, Demircan, Mesut, Derksen, Chris, Deus, Ricardo, Dhurmea, KR, Diamond, Howard J, Dirkse, S, Divine, Dmitry, Dokulil, Martin T, Donat, Markus G, Dong, Shenfu, Dorigo, Wouter A, Drost Jensen, Caroline, Druckenmiller, Matthew L, Drumond, Paula, du Plessis, Marcel, Dugan, Hilary A, Dulamsuren, Dashkhuu, Dunmire, Devon, Dunn, Robert JH, Durre, Imke, Dutton, Geoff, Duveiller, Gregory, Ekici, Mithat, Elias Chereque, Alesksandra, ElKharrim, M, Epstein, Howard E, Espinoza, Jhan-Carlo, Estilow, Thomas W, Estrella, Nicole, Fauchereau, Nicolas, Fausto, Robert S, Feely, Richard A, Fenimore, Chris, Fereday, David, Fettweis, Xavier, Fioletov, vitali E, Flemming, Johannes, Fogarty, Chris, Fogt, Ryan L, Forbes, Bruce C, Foster, Michael J, Franz, Bryan A, Freeman, Natalie M, Fricker, Helen A, Frith, Stacey M, Froidevaux, Lucien, Frost, Gerald V, Fuhrman, Steven, Füllekrug, Martin, Ganter, Catherine, Gao, Meng, Gardner, Alex S, Garforth, Judith, Garg, Jay, Gerland, Sebastian, Gibbes, Badin, Gille, Sarah T, Gilson, John, Gleason, Karin, Gobron, Nadine, Goetz, Scott J, Goldenberg, Stanley B, Goni, Gustavo, Goodman, Steven, Goto, Atsushi, Grooß, Jens-Uwe, Gruber, Alexander, Gu, Guojun, Guard, Charles Chip P, Hagos, S, Hahn, Sebastian, Haimberger, Leopold, Hall, Bradley D, Hamlington, Benjamin D, Hanna, Edward, Hanssen-Bauer, Inger, Harnos, Daniel S, Harris, Ian, He, Qiong, Heim, Richard R, Hellström, Sverker, Hemming, Deborah L, Hendricks, Stefan, Hicks, J, Hidalgo, Hugo G, Hirschi, Martin, Ho, Shu-peng, Hobbs, W, Holmes, Robert M, Holzworth, Robert, Hrbáček, Filip, Hu, Guojie, Hu, Zeng-Zhen, Huang, Boyin, Huang, Hongjie, Hurst, Dale F, Ialongo, Iolanda, Inness, Antje, Isaksen, Ketil, Ishii, Masayoshi, Jadra, Gerardo, Jevrejeva, Svetlana, John, Viju O, Johns, W, Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C, Jones, Philip D, Jones, Timothy, Josey, Simon A, Jumaux, G, Junod, Robert, Kääb, Andreas, Kabidi, K, Kaiser, Johannes W, Kaler, Robb SA, Kaleschke, Lars, Kaufmann, Viktor, Kazemi, Amin Fazl, Keller, Linda M, Kellerer-Pirklbauer, Andreas, Kendon, Mike, Kennedy, John, Kent, Elizabeth C, Kerr, Kenneth, Khan, Valentina, Khiem, Mai Van, Kidd, Richard, Kim, Mi Ju, Kim, Seong-Joong, Kipling, Zak, Klotzbach, Philip J, Knaff, John A, Koppa, Akash, Korshunova, Natalia N, Kraemer, Benjamin M, Kramarova, Natalya A, Kruger, AC, Kruger, Andries, Kumar, Arun, L’Heureux, Michelle, La Fuente, Sofia, Laas, Alo, Labe, Zachary M, Lader, Rick, Lakatos, Mónika, Lakkala, Kaisa, Lam, Hoang Phuc, Lan, Xin, Landschützer, Peter, Landsea, Chris W, Lang, Timothy, Lankhorst, Matthias, Lantz, Kathleen O, Lara, Mark J, Lavado-Casimiro, Waldo, Lavers, David A, Lazzara, Matthew A, Leblanc, Thierry, Lee, Tsz-Cheung, Leibensperger, Eric M, Lennard, Chris, Leuliette, Eric, Leung, Kinson HY, Lieser, Jan L, Likso, Tanja, Lin, I-I, Lindsey, Jackie, Liu, Yakun, Locarnini, Ricardo, Loeb, Norman G, Loomis, Bryant D, Lorrey, Andrew M, Loyola, Diego, Lu, Rui, Lumpkin, Rick, Luo, Jing-Jia, Luojus, Kari, Lyman, John M, Maberly, Stephen C, Macander, Matthew J, MacFerrin, Michael, MacGilchrist, Graeme A, MacLennan, Michelle L, Madelon, Remi, Magee, Andrew D, Magnin, Florence, Mamen, Jostein, Mankoff, Ken D, Manney, Gloria L, Marcinonienė, Izolda, Marengo, Jose A, Marjan, Mohammadi, Martínez, Ana E, Massom, Robert A, Matsuzaki, Shin-Ichiro, May, Linda, Mayer, Michael, Mazloff, Matthew R, McAfee, Stephanie A, McBride, C, McCabe, Matthew F, McClelland, James W, McPhaden, Michael J, Mcvicar, Tim R, Mears, Carl A, Meier, Walter N, Mekonnen, A, Menzel, Annette, Merchant, Christopher J, Merrifield, Mark A, Meyer, Michael F, Meyers, Tristan, Mikolajczyk, David E, Miller, John B, Miralles, Diego G, Misevicius, Noelia, Mishonov, Alexey, Mitchum, Gary T, Moat, Ben I, Moesinger, Leander, Moise, Aurel, Molina-Carpio, Jorge, Monet, Ghislaine, Montzka, Stephan A, Moon, Twila A, Moore, GWK, Mora, Natali, Morán, Johnny, Morehen, Claire, Morice, Colin, Mostafa, AE, Mote, Thomas L, Mrekaj, Ivan, Mudryk, Lawrence, Mühle, Jens, Müller, Rolf, Nance, David, Nash, Eric R, Nerem, R Steven, Newman, Paul A, Nicolas, Julien P, Nieto, Juan J, Noetzli, Jeannette, Noll, Ben, Norton, Taylor, Nyland, Kelsey E, O’Keefe, John, Ochwat, Naomi, Oikawa, Yoshinori, Okunaka, Yuka, Osborn, Timothy J, Overland, James E, Park, Taejin, Parrington, Mark, Parrish, Julia K, Pasch, Richard J, Pascual Ramírez, Reynaldo, Pellet, Cécile, Pelto, Mauri S, Perčec Tadić, Melita, Perovich, Donald K, Petersen, Guðrún Nína, Petersen, Kyle, Petropavlovskikh, Irina, Petty, Alek, Pezza, Alexandre B, Pezzi, Luciano P, Phillips, Coda, Phoenix, Gareth K, Pierson, Don, Pinto, Izidine, Pires, Vanda, Pitts, Michael, Po-Chedley, Stephen, Pogliotti, Paolo, Poinar, Kristin, Polvani, Lorenzo, Preimesberger, Wolfgang, Price, Colin, Pulkkanen, Merja, Purkey, Sarah G, Qiu, Bo, Quisbert, Kenny, Quispe, Willy R, Rajeevan, M, Ramos, Andrea M, Randel, William J, Rantanen, Mika, Raphael, Marilyn N, Reagan, James, Recalde, Cristina, Reid, Phillip, Rémy, Samuel, Reyes Kohler, Alejandra J, Ricciardulli, Lucrezia, Richardson, Andrew D, Ricker, Robert, Robinson, David A, Robjhon, M, Rocha, Willy, Rodell, Matthew, Rodriguez Guisado, Esteban, Rodriguez-Fernandez, Nemesio, Romanovsky, Vladimir E, Ronchail, Josyane, Rosencrans, Matthew, Rosenlof, Karen H, Rösner, Benjamin, Rösner, Henrieke, Rozanov, Alexei, Rozkošný, Jozef, Rubek, Frans, Rusanovskaya, Olga O, Rutishauser, This, Sabeerali, CT, Salinas, Roberto, Sánchez-Lugo, Ahira, Santee, Michelle L, Santini, Marcelo, Sato, Katsunari, Sawaengphokhai, Parnchai, Sayouri, A, Scambos, Theodore, Schenzinger, Verena, Schimanke, Semjon, Schlegel, Robert W, Schmid, Claudia, Schmid, Martin, Schneider, Udo, Schreck, Carl J, Schultz, Cristina, _, _, Segele, ZT, Sensoy, Serhat, Serbin, Shawn P, Serreze, Mark C, Setiawan, Amsari Mudzakir, Sezaki, Fumi, Sharma, Sapna, Sharp, Jonathan D, Sheffield, Gay, Shi, Jia-Rui, Shi, Lei, Shiklomanov, Alexander I, Shiklomanov, Nikolay I, Shimaraeva, Svetlana V, Shukla, R, Siegel, David A, Silow, Eugene A, Sima, F, Simmons, Adrian J, Smeed, David A, Smith, Adam, Smith, Sharon L, Soden, Brian J, Sofieva, Viktoria, Souza, Everaldo, Sparks, Tim H, Spence-Hemmings, Jacqueline, Spencer, Robert GM, Spillane, Sandra, Sreejith, OP, Srivastava, AK, Stackhouse, Paul W, Stammerjohn, Sharon, Stauffer, Ryan, Steinbrecht, Wolfgang, Steiner, Andrea K, Stella, Jose L, Stephenson, Tannecia S, Stradiotti, Pietro, Strahan, Susan E, Streletskiy, Dmitry A, Surendran, Divya E, Suslova, Anya, Svendby, Tove, Sweet, William, Takahashi, Kiyotoshi, Takemura, Kazuto, Tank, Suzanne E, Taylor, Michael A, Tedesco, Marco, Thackeray, Stephen J, Thiaw, WM, Thibert, Emmanuel, Thoman, Richard L, Thompson, Andrew F, Thompson, Philip R, Tian-Kunze, Xiangshan, Timmermans, Mary-Louise, Timofeyev, Maxim A, Tobin, Skie, Tømmervik, Hans, Tourpali, Kleareti, Trescilo, Lidia, Tretiakov, Mikhail, Trewin, Blair C, Triñanes, Joaquin A, Trotman, Adrian, Truchelut, Ryan E, Trusel, Luke D, Tye, Mari R, van der A, Ronald, van der Schalie, Robin, van der Schrier, Gerard, Van Meerbeeck, Cedric J, van vliet, Arnold JH, Vazife, Ahad, Verburg, Piet, Vernier, Jean-Paul, Vimont, Isaac J, Virts, Katrina, Vivero, Sebastián, Volkov, Denis L, Vömel, Holger, Vose, Russell S, Walker, Donald A, Walsh, John E, Wang, Bin, Wang, Hui, Wang, Muyin, Wang, Ray HJ, Wang, Xinyue, Wanninkhof, Rik, Warnock, Taran, Weber, Mark, Webster, Melinda, Wehrlé, Adrian, Wen, Caihong, Westberry, Toby K, Widlansky, Matthew J, Wiese, David N, Wild, Jeannette D, Wille, Jonathan D, Willems, An, Willett, Kate M, Williams, Earle, Willis, J, Wong, Takmeng, Wood, Kimberly M, Woolway, Richard Iestyn, Xie, Ping-Ping, Yang, Dedi, Yin, Xungang, Yin, Ziqi, Zeng, Zhenzhong, Zhang, Huai-min, Zhang, Li, Zhang, Peiqun, Zhao, Lin, Zhou, Xinjia, Zhu, Zhiwei, Ziemke, Jerry R, Ziese, Markus, Zolkos, Scott, Zotta, Ruxandra M, Zou, Cheng-Zhi, Allen, Jessicca, Camper, Amy V, Haley, Bridgette O, Hammer, Gregory, Love-Brotak, S Elizabeth, Ohlmann, Laura, Noguchi, Lukas, Riddle, Deborah B, Veasey, Sara W, Boyer, Tim, Bartow-Gillies, Ellen, Abida, A, Ades, Melanie, Adler, Robert, Adusumilli, Susheel, Agyakwah, W, Ahmasuk, Brandon, Aldeco, Laura S, Alexe, Mihai, Alfaro, Eric J, Allan, Richard P, Allgood, Adam, Alves, Lincoln M, Amador, Jorge A, Anderson, John, Andrade, B, Anneville, Orlane, Aono, Yasuyuki, Arguez, Anthony, Arosio, Carlo, Atkinson, C, Augustine, John A, Avalos, Grinia, Azorin-Molina, Cesar, Backensto, Stacia A, Bader, Stephan, Baez, Julian, Baiman, Rebecca, Ballinger, Thomas J, Banwell, Alison F, Bardin, M Yu, Barichivich, Jonathan, Barnes, John E, Barreira, Sandra, Beadling, Rebecca L, Beck, Hylke E, Becker, Emily J, Bekele, E, Bellido, Guillem Martín, Bellouin, Nicolas, Benedetti, Angela, Benestad, Rasmus, Berne, Christine, Berner, Logan T, Bernhard, Germar H, Bhatt, Uma S, Bhuiyan, AE, Bigalke, Siiri, Biló, Tiago, Bissolli, Peter, Bjerke Jarle, W, Blagrave, Kevin, Blake, Eric S, Blenkinsop, Stephen, Blunden, Jessica, Bochníček, Oliver, Bock, Olivier, Bodin, Xavier, Bosilovich, Michael, Boucher, Olivier, Bozkurt, Deniz, Brettschneider, Brian, Bringas, Francis G, Bringas, Francis, Buechler, Dennis, Buehler, Stefan A, Bukunt, Brandon, Calderón, Blanca, Camargo, Suzana J, Campbell, Jayaka, Campos, Diego, Carrea, Laura, Carter, Brendan R, Cetinić, Ivona, Chambers, Don P, Chan, Duo, Chandler, Elise, Chang, Kai-Lan, Chen, Hua, Chen, Lin, Cheng, Lijing, Cheng, Vincent YS, Chomiak, Leah, Christiansen, Hanne H, Christy, John R, Chung, Eui-Seok, Ciasto, Laura M, Clarke, Leonardo, Clem, Kyle R, Clingan, Scott, Coelho, Caio AS, Cohen, Judah L, Coldewey-Egbers, Melanie, Colwell, Steve, Cooper, Owen R, Cornes, Richard C, Correa, Kris, Costa, Felipe, Covey, Curt, Coy, Lawrence, Créatux, Jean-François, Crhova, Lenka, Crimmins, Theresa, Cronin, Meghan F, Cropper, Thomas, Crotwell, Molly, Culpepper, Joshua, Cunha, Ana P, Cusicanqui, Diego, Datta, Rajashree T, Davis, Sean M, De Bock, Veerle, de Jeu, Richard AM, De Laat, Jos, Decharme, Bertrand, Degenstein, Doug, Delaloye, Reynald, Demircan, Mesut, Derksen, Chris, Deus, Ricardo, Dhurmea, KR, Diamond, Howard J, Dirkse, S, Divine, Dmitry, Dokulil, Martin T, Donat, Markus G, Dong, Shenfu, Dorigo, Wouter A, Drost Jensen, Caroline, Druckenmiller, Matthew L, Drumond, Paula, du Plessis, Marcel, Dugan, Hilary A, Dulamsuren, Dashkhuu, Dunmire, Devon, Dunn, Robert JH, Durre, Imke, Dutton, Geoff, Duveiller, Gregory, Ekici, Mithat, Elias Chereque, Alesksandra, ElKharrim, M, Epstein, Howard E, Espinoza, Jhan-Carlo, Estilow, Thomas W, Estrella, Nicole, Fauchereau, Nicolas, Fausto, Robert S, Feely, Richard A, Fenimore, Chris, Fereday, David, Fettweis, Xavier, Fioletov, vitali E, Flemming, Johannes, Fogarty, Chris, Fogt, Ryan L, Forbes, Bruce C, Foster, Michael J, Franz, Bryan A, Freeman, Natalie M, Fricker, Helen A, Frith, Stacey M, Froidevaux, Lucien, Frost, Gerald V, Fuhrman, Steven, Füllekrug, Martin, Ganter, Catherine, Gao, Meng, Gardner, Alex S, Garforth, Judith, Garg, Jay, Gerland, Sebastian, Gibbes, Badin, Gille, Sarah T, Gilson, John, Gleason, Karin, Gobron, Nadine, Goetz, Scott J, Goldenberg, Stanley B, Goni, Gustavo, Goodman, Steven, Goto, Atsushi, Grooß, Jens-Uwe, Gruber, Alexander, Gu, Guojun, Guard, Charles Chip P, Hagos, S, Hahn, Sebastian, Haimberger, Leopold, Hall, Bradley D, Hamlington, Benjamin D, Hanna, Edward, Hanssen-Bauer, Inger, Harnos, Daniel S, Harris, Ian, He, Qiong, Heim, Richard R, Hellström, Sverker, Hemming, Deborah L, Hendricks, Stefan, Hicks, J, Hidalgo, Hugo G, Hirschi, Martin, Ho, Shu-peng, Hobbs, W, Holmes, Robert M, Holzworth, Robert, Hrbáček, Filip, Hu, Guojie, Hu, Zeng-Zhen, Huang, Boyin, Huang, Hongjie, Hurst, Dale F, Ialongo, Iolanda, Inness, Antje, Isaksen, Ketil, Ishii, Masayoshi, Jadra, Gerardo, Jevrejeva, Svetlana, John, Viju O, Johns, W, Johnsen, Bjørn, Johnson, Bryan, Johnson, Gregory C, Jones, Philip D, Jones, Timothy, Josey, Simon A, Jumaux, G, Junod, Robert, Kääb, Andreas, Kabidi, K, Kaiser, Johannes W, Kaler, Robb SA, Kaleschke, Lars, Kaufmann, Viktor, Kazemi, Amin Fazl, Keller, Linda M, Kellerer-Pirklbauer, Andreas, Kendon, Mike, Kennedy, John, Kent, Elizabeth C, Kerr, Kenneth, Khan, Valentina, Khiem, Mai Van, Kidd, Richard, Kim, Mi Ju, Kim, Seong-Joong, Kipling, Zak, Klotzbach, Philip J, Knaff, John A, Koppa, Akash, Korshunova, Natalia N, Kraemer, Benjamin M, Kramarova, Natalya A, Kruger, AC, Kruger, Andries, Kumar, Arun, L’Heureux, Michelle, La Fuente, Sofia, Laas, Alo, Labe, Zachary M, Lader, Rick, Lakatos, Mónika, Lakkala, Kaisa, Lam, Hoang Phuc, Lan, Xin, Landschützer, Peter, Landsea, Chris W, Lang, Timothy, Lankhorst, Matthias, Lantz, Kathleen O, Lara, Mark J, Lavado-Casimiro, Waldo, Lavers, David A, Lazzara, Matthew A, Leblanc, Thierry, Lee, Tsz-Cheung, Leibensperger, Eric M, Lennard, Chris, Leuliette, Eric, Leung, Kinson HY, Lieser, Jan L, Likso, Tanja, Lin, I-I, Lindsey, Jackie, Liu, Yakun, Locarnini, Ricardo, Loeb, Norman G, Loomis, Bryant D, Lorrey, Andrew M, Loyola, Diego, Lu, Rui, Lumpkin, Rick, Luo, Jing-Jia, Luojus, Kari, Lyman, John M, Maberly, Stephen C, Macander, Matthew J, MacFerrin, Michael, MacGilchrist, Graeme A, MacLennan, Michelle L, Madelon, Remi, Magee, Andrew D, Magnin, Florence, Mamen, Jostein, Mankoff, Ken D, Manney, Gloria L, Marcinonienė, Izolda, Marengo, Jose A, Marjan, Mohammadi, Martínez, Ana E, Massom, Robert A, Matsuzaki, Shin-Ichiro, May, Linda, Mayer, Michael, Mazloff, Matthew R, McAfee, Stephanie A, McBride, C, McCabe, Matthew F, McClelland, James W, McPhaden, Michael J, Mcvicar, Tim R, Mears, Carl A, Meier, Walter N, Mekonnen, A, Menzel, Annette, Merchant, Christopher J, Merrifield, Mark A, Meyer, Michael F, Meyers, Tristan, Mikolajczyk, David E, Miller, John B, Miralles, Diego G, Misevicius, Noelia, Mishonov, Alexey, Mitchum, Gary T, Moat, Ben I, Moesinger, Leander, Moise, Aurel, Molina-Carpio, Jorge, Monet, Ghislaine, Montzka, Stephan A, Moon, Twila A, Moore, GWK, Mora, Natali, Morán, Johnny, Morehen, Claire, Morice, Colin, Mostafa, AE, Mote, Thomas L, Mrekaj, Ivan, Mudryk, Lawrence, Mühle, Jens, Müller, Rolf, Nance, David, Nash, Eric R, Nerem, R Steven, Newman, Paul A, Nicolas, Julien P, Nieto, Juan J, Noetzli, Jeannette, Noll, Ben, Norton, Taylor, Nyland, Kelsey E, O’Keefe, John, Ochwat, Naomi, Oikawa, Yoshinori, Okunaka, Yuka, Osborn, Timothy J, Overland, James E, Park, Taejin, Parrington, Mark, Parrish, Julia K, Pasch, Richard J, Pascual Ramírez, Reynaldo, Pellet, Cécile, Pelto, Mauri S, Perčec Tadić, Melita, Perovich, Donald K, Petersen, Guðrún Nína, Petersen, Kyle, Petropavlovskikh, Irina, Petty, Alek, Pezza, Alexandre B, Pezzi, Luciano P, Phillips, Coda, Phoenix, Gareth K, Pierson, Don, Pinto, Izidine, Pires, Vanda, Pitts, Michael, Po-Chedley, Stephen, Pogliotti, Paolo, Poinar, Kristin, Polvani, Lorenzo, Preimesberger, Wolfgang, Price, Colin, Pulkkanen, Merja, Purkey, Sarah G, Qiu, Bo, Quisbert, Kenny, Quispe, Willy R, Rajeevan, M, Ramos, Andrea M, Randel, William J, Rantanen, Mika, Raphael, Marilyn N, Reagan, James, Recalde, Cristina, Reid, Phillip, Rémy, Samuel, Reyes Kohler, Alejandra J, Ricciardulli, Lucrezia, Richardson, Andrew D, Ricker, Robert, Robinson, David A, Robjhon, M, Rocha, Willy, Rodell, Matthew, Rodriguez Guisado, Esteban, Rodriguez-Fernandez, Nemesio, Romanovsky, Vladimir E, Ronchail, Josyane, Rosencrans, Matthew, Rosenlof, Karen H, Rösner, Benjamin, Rösner, Henrieke, Rozanov, Alexei, Rozkošný, Jozef, Rubek, Frans, Rusanovskaya, Olga O, Rutishauser, This, Sabeerali, CT, Salinas, Roberto, Sánchez-Lugo, Ahira, Santee, Michelle L, Santini, Marcelo, Sato, Katsunari, Sawaengphokhai, Parnchai, Sayouri, A, Scambos, Theodore, Schenzinger, Verena, Schimanke, Semjon, Schlegel, Robert W, Schmid, Claudia, Schmid, Martin, Schneider, Udo, Schreck, Carl J, Schultz, Cristina, _, _, Segele, ZT, Sensoy, Serhat, Serbin, Shawn P, Serreze, Mark C, Setiawan, Amsari Mudzakir, Sezaki, Fumi, Sharma, Sapna, Sharp, Jonathan D, Sheffield, Gay, Shi, Jia-Rui, Shi, Lei, Shiklomanov, Alexander I, Shiklomanov, Nikolay I, Shimaraeva, Svetlana V, Shukla, R, Siegel, David A, Silow, Eugene A, Sima, F, Simmons, Adrian J, Smeed, David A, Smith, Adam, Smith, Sharon L, Soden, Brian J, Sofieva, Viktoria, Souza, Everaldo, Sparks, Tim H, Spence-Hemmings, Jacqueline, Spencer, Robert GM, Spillane, Sandra, Sreejith, OP, Srivastava, AK, Stackhouse, Paul W, Stammerjohn, Sharon, Stauffer, Ryan, Steinbrecht, Wolfgang, Steiner, Andrea K, Stella, Jose L, Stephenson, Tannecia S, Stradiotti, Pietro, Strahan, Susan E, Streletskiy, Dmitry A, Surendran, Divya E, Suslova, Anya, Svendby, Tove, Sweet, William, Takahashi, Kiyotoshi, Takemura, Kazuto, Tank, Suzanne E, Taylor, Michael A, Tedesco, Marco, Thackeray, Stephen J, Thiaw, WM, Thibert, Emmanuel, Thoman, Richard L, Thompson, Andrew F, Thompson, Philip R, Tian-Kunze, Xiangshan, Timmermans, Mary-Louise, Timofeyev, Maxim A, Tobin, Skie, Tømmervik, Hans, Tourpali, Kleareti, Trescilo, Lidia, Tretiakov, Mikhail, Trewin, Blair C, Triñanes, Joaquin A, Trotman, Adrian, Truchelut, Ryan E, Trusel, Luke D, Tye, Mari R, van der A, Ronald, van der Schalie, Robin, van der Schrier, Gerard, Van Meerbeeck, Cedric J, van vliet, Arnold JH, Vazife, Ahad, Verburg, Piet, Vernier, Jean-Paul, Vimont, Isaac J, Virts, Katrina, Vivero, Sebastián, Volkov, Denis L, Vömel, Holger, Vose, Russell S, Walker, Donald A, Walsh, John E, Wang, Bin, Wang, Hui, Wang, Muyin, Wang, Ray HJ, Wang, Xinyue, Wanninkhof, Rik, Warnock, Taran, Weber, Mark, Webster, Melinda, Wehrlé, Adrian, Wen, Caihong, Westberry, Toby K, Widlansky, Matthew J, Wiese, David N, Wild, Jeannette D, Wille, Jonathan D, Willems, An, Willett, Kate M, Williams, Earle, Willis, J, Wong, Takmeng, Wood, Kimberly M, Woolway, Richard Iestyn, Xie, Ping-Ping, Yang, Dedi, Yin, Xungang, Yin, Ziqi, Zeng, Zhenzhong, Zhang, Huai-min, Zhang, Li, Zhang, Peiqun, Zhao, Lin, Zhou, Xinjia, Zhu, Zhiwei, Ziemke, Jerry R, Ziese, Markus, Zolkos, Scott, Zotta, Ruxandra M, Zou, Cheng-Zhi, Allen, Jessicca, Camper, Amy V, Haley, Bridgette O, Hammer, Gregory, Love-Brotak, S Elizabeth, Ohlmann, Laura, Noguchi, Lukas, Riddle, Deborah B, and Veasey, Sara W

Abstract

Abstract —J. BLUNDEN, T. BOYER, AND E. BARTOW-GILLIES Earth’s global climate system is vast, complex, and intricately interrelated. Many areas are influenced by global-scale phenomena, including the “triple dip” La Niña conditions that prevailed in the eastern Pacific Ocean nearly continuously from mid-2020 through all of 2022; by regional phenomena such as the positive winter and summer North Atlantic Oscillation that impacted weather in parts the Northern Hemisphere and the negative Indian Ocean dipole that impacted weather in parts of the Southern Hemisphere; and by more localized systems such as high-pressure heat domes that caused extreme heat in different areas of the world. Underlying all these natural short-term variabilities are long-term climate trends due to continuous increases since the beginning of the Industrial Revolution in the atmospheric concentrations of Earth’s major greenhouse gases. In 2022, the annual global average carbon dioxide concentration in the atmosphere rose to 417.1±0.1 ppm, which is 50% greater than the pre-industrial level. Global mean tropospheric methane abundance was 165% higher than its pre-industrial level, and nitrous oxide was 24% higher. All three gases set new record-high atmospheric concentration levels in 2022. Sea-surface temperature patterns in the tropical Pacific characteristic of La Niña and attendant atmospheric patterns tend to mitigate atmospheric heat gain at the global scale, but the annual global surface temperature across land and oceans was still among the six highest in records dating as far back as the mid-1800s. It was the warmest La Niña year on record. Many areas observed record or near-record heat. Europe as a whole observed its second-warmest year on record, with sixteen individual countries observing record warmth at the national scale. Records were shattered across the continent during the summer months as heatwaves

Published
2023

17. Acquired benign tracheo‑oesophageal fistula secondary to oesophageal stenting for post lye ingestion stenosis: A case report

Author

Bolca, Ciprian, primary, Bobocea, Andrei, additional, Cosoveanu, George, additional, Alexe, Mihai, additional, Cadar, Genoveva, additional, Balescu, Irina, additional, Bacalbasa, Nicolae, additional, Gherghiceanu, Florentina, additional, Furtunescu, Florentina, additional, Radavoi, Daniel, additional, Paleru, Cristian, additional, Miron, Adrian, additional, and Cordos, Ioan, additional

Published
2022
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23. Variability and quasi-decadal changes in the methane budget over the period 2000–2012

Author

Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences, Prinn, Ronald G, Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G., Dlugokencky, Edward J., Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N., Castaldi, Simona, Jackson, Robert B., Alexe, Mihai, Arora, Vivek K., Beerling, David J., Bergamaschi, Peter, Blake, Donald R., Brailsford, Gordon, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Covey, Kristofer, Frankenberg, Christian, Gedney, Nicola, Höglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-François, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, Melton, Joe R., Morino, Isamu, Naik, Vaishali, O'Doherty, Simon, Parmentier, Frans-Jan W., Patra, Prabir K., Peng, Changhui, Peng, Shushi, Peters, Glen P., Pison, Isabelle, Ramonet, Michel, Riley, William J., Saito, Makoto, Santini, Monia, Schroeder, Ronny, Simpson, Isobel J., Spahni, Renato, Takizawa, Atsushi, Thornton, Brett F., Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, Weiss, Ray, Wilton, David J., Wiltshire, Andy, Worthy, Doug, Wunch, Debra, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, Zhu, Qiuan, Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences, Prinn, Ronald G, Saunois, Marielle, Bousquet, Philippe, Poulter, Ben, Peregon, Anna, Ciais, Philippe, Canadell, Josep G., Dlugokencky, Edward J., Etiope, Giuseppe, Bastviken, David, Houweling, Sander, Janssens-Maenhout, Greet, Tubiello, Francesco N., Castaldi, Simona, Jackson, Robert B., Alexe, Mihai, Arora, Vivek K., Beerling, David J., Bergamaschi, Peter, Blake, Donald R., Brailsford, Gordon, Bruhwiler, Lori, Crevoisier, Cyril, Crill, Patrick, Covey, Kristofer, Frankenberg, Christian, Gedney, Nicola, Höglund-Isaksson, Lena, Ishizawa, Misa, Ito, Akihiko, Joos, Fortunat, Kim, Heon-Sook, Kleinen, Thomas, Krummel, Paul, Lamarque, Jean-François, Langenfelds, Ray, Locatelli, Robin, Machida, Toshinobu, Maksyutov, Shamil, Melton, Joe R., Morino, Isamu, Naik, Vaishali, O'Doherty, Simon, Parmentier, Frans-Jan W., Patra, Prabir K., Peng, Changhui, Peng, Shushi, Peters, Glen P., Pison, Isabelle, Ramonet, Michel, Riley, William J., Saito, Makoto, Santini, Monia, Schroeder, Ronny, Simpson, Isobel J., Spahni, Renato, Takizawa, Atsushi, Thornton, Brett F., Tian, Hanqin, Tohjima, Yasunori, Viovy, Nicolas, Voulgarakis, Apostolos, Weiss, Ray, Wilton, David J., Wiltshire, Andy, Worthy, Doug, Wunch, Debra, Xu, Xiyan, Yoshida, Yukio, Zhang, Bowen, Zhang, Zhen, and Zhu, Qiuan

Abstract

Following the recent Global Carbon Project (GCP) synthesis of the decadal methane (CH₄) budget over 2000-2012 (Saunois et al., 2016), we analyse here the same dataset with a focus on quasi-decadal and inter-annual variability in CH₄ emissions. The GCP dataset integrates results from top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models (including process-based models for estimating land surface emissions and atmospheric chemistry), inventories of anthropogenic emissions, and data-driven approaches. The annual global methane emissions from top-down studies, which by construction match the observed methane growth rate within their uncertainties, all show an increase in total methane emissions over the period 2000-2012, but this increase is not linear over the 13 years. Despite differences between individual studies, the mean emission anomaly of the top-down ensemble shows no significant trend in total methane emissions over the period 2000-2006, during the plateau of atmospheric methane mole fractions, and also over the period 2008-2012, during the renewed atmospheric methane increase. However, the top-down ensemble mean produces an emission shift between 2006 and 2008, leading to 22 [16-32]Tg CH₄ yr⁻¹ higher methane emissions over the period 2008-2012 compared to 2002-2006. This emission increase mostly originated from the tropics, with a smaller contribution from mid-latitudes and no significant change from boreal regions. The regional contributions remain uncertain in top-down studies. Tropical South America and South and East Asia seem to contribute the most to the emission increase in the tropics. However, these two regions have only limited atmospheric measurements and remain therefore poorly constrained. The sectorial partitioning of this emission increase between the periods 2002-2006 and 2008-2012 differs from one atmospheric inversion study to another. However, all top-down studies sugge

Published
2018

24. The GHG-CCI project of ESA's climate change initiative:Data products and application

Author

Buchwitz, M., Reuter, M., Schneising, O., Boesch, Hartmut, Aben, I., Alexe, Mihai, Bergamaschi, P., Bovensmann, H., Brunner, D, Buchmann, B., Burrows, J. P., Butz, A., Chevallier, F., Crevoisier, C. D., De Mazière, M., De Wachter, E., Detmers, R., Dils, B., Feng, L., Frankenberg, C., Hasekamp, O.P., Hewson, W., Heymann, J., Houweling, S., Kaminski, T., Laeng, A., van Leeuwen, T., Lichtenberg, G., Marshall, J., Noël, S., Notholt, J., Palmer, P.I., Parker, R., Sundström, A. M., Scholze, M., Stiller, G. P., Warneke, T., and Zehner, C.

Subjects
SDG 13 - Climate Action
Abstract

The goal of the GHG-CCI project (http://www.esa-ghg-cci.org/) of ESA's Climate Change Initiative (CCI) is to generate global atmospheric satellite-derived carbon dioxide (CO2) and methane (CH4) data sets as needed to improve our understanding of the regional sources and sinks of these important greenhouse gases (GHG). Here we present an overview about the latest data set called Climate Research Data Package No. 3 (CRDP3). We focus on the GHG-CCI project core data products, which are near-surface-sensitive column-averaged dry air mole fractions of CO2 and CH4, denoted XCO2 (in ppm) and XCH4 (in ppb) retrieved from SCIAMACHY/ENVISAT (2002-2012) and TANSO-FTS/GOSAT (2009-today) nadir mode radiance observations in the near-infrared/shortwave-infrared spectral region. The GHG-CCI products are primarily individual sensor Level 2 products. However, we also generate merged Level 2 products ("EMMA products"). Here we also present a first GHG-CCI Level 3 product, namely XCO2 and XCH4 in Obs4MIPs format (monthly, 5°×5°).

Published
2016

25. Variability and quasi-decadal changes in the methane budget over the period 2000–2012

Author

Saunois, Marielle, primary, Bousquet, Philippe, additional, Poulter, Benjamin, additional, Peregon, Anna, additional, Ciais, Philippe, additional, Canadell, Josep G., additional, Dlugokencky, Edward J., additional, Etiope, Giuseppe, additional, Bastviken, David, additional, Houweling, Sander, additional, Janssens-Maenhout, Greet, additional, Tubiello, Francesco N., additional, Castaldi, Simona, additional, Jackson, Robert B., additional, Alexe, Mihai, additional, Arora, Vivek K., additional, Beerling, David J., additional, Bergamaschi, Peter, additional, Blake, Donald R., additional, Brailsford, Gordon, additional, Bruhwiler, Lori, additional, Crevoisier, Cyril, additional, Crill, Patrick, additional, Covey, Kristofer, additional, Frankenberg, Christian, additional, Gedney, Nicola, additional, Höglund-Isaksson, Lena, additional, Ishizawa, Misa, additional, Ito, Akihiko, additional, Joos, Fortunat, additional, Kim, Heon-Sook, additional, Kleinen, Thomas, additional, Krummel, Paul, additional, Lamarque, Jean-François, additional, Langenfelds, Ray, additional, Locatelli, Robin, additional, Machida, Toshinobu, additional, Maksyutov, Shamil, additional, Melton, Joe R., additional, Morino, Isamu, additional, Naik, Vaishali, additional, O'Doherty, Simon, additional, Parmentier, Frans-Jan W., additional, Patra, Prabir K., additional, Peng, Changhui, additional, Peng, Shushi, additional, Peters, Glen P., additional, Pison, Isabelle, additional, Prinn, Ronald, additional, Ramonet, Michel, additional, Riley, William J., additional, Saito, Makoto, additional, Santini, Monia, additional, Schroeder, Ronny, additional, Simpson, Isobel J., additional, Spahni, Renato, additional, Takizawa, Atsushi, additional, Thornton, Brett F., additional, Tian, Hanqin, additional, Tohjima, Yasunori, additional, Viovy, Nicolas, additional, Voulgarakis, Apostolos, additional, Weiss, Ray, additional, Wilton, David J., additional, Wiltshire, Andy, additional, Worthy, Doug, additional, Wunch, Debra, additional, Xu, Xiyan, additional, Yoshida, Yukio, additional, Zhang, Bowen, additional, Zhang, Zhen, additional, and Zhu, Qiuan, additional

Published
2017
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26. Supplementary material to "Variability and quasi-decadal changes in the methane budget over the period 2000–2012"

Author

Saunois, Marielle, primary, Bousquet, Philippe, additional, Poulter, Benjamin, additional, Peregon, Anna, additional, Ciais, Philippe, additional, Canadell, Josep G., additional, Dlugokencky, Edward J., additional, Etiope, Giuseppe, additional, Bastviken, David, additional, Houweling, Sander, additional, Janssens-Maenhout, Greet, additional, Tubiello, Francesco N., additional, Castaldi, Simona, additional, Jackson, Robert B., additional, Alexe, Mihai, additional, Arora, Vivek K., additional, Beerling, David J., additional, Bergamaschi, Peter, additional, Blake, Donald R., additional, Brailsford, Gordon, additional, Bruhwiler, Lori, additional, Crevoisier, Cyril, additional, Crill, Patrick, additional, Covey, Kristofer, additional, Frankenberg, Christian, additional, Gedney, Nicola, additional, Höglund-Isaksson, Lena, additional, Ishizawa, Misa, additional, Ito, Akihiko, additional, Joos, Fortunat, additional, Kim, Heon-Sook, additional, Kleinen, Thomas, additional, Krummel, Paul, additional, Lamarque, Jean-François, additional, Langenfelds, Ray, additional, Locatelli, Robin, additional, Machida, Toshinobu, additional, Maksyutov, Shamil, additional, Melton, Joe R., additional, Morino, Isamu, additional, Naik, Vaishali, additional, O'Doherty, Simon, additional, Parmentier, Frans-Jan W., additional, Patra, Prabir K., additional, Peng, Changhui, additional, Peng, Shushi, additional, Peters, Glen P., additional, Pison, Isabelle, additional, Prinn, Ronald, additional, Ramonet, Michel, additional, Riley, William J., additional, Saito, Makoto, additional, Santini, Monia, additional, Schroeder, Ronny, additional, Simpson, Isobel J., additional, Spahni, Renato, additional, Takizawa, Atsushi, additional, Thornton, Brett F., additional, Tian, Hanqin, additional, Tohjima, Yasunori, additional, Viovy, Nicolas, additional, Voulgarakis, Apostolos, additional, Weiss, Ray, additional, Wilton, David J., additional, Wiltshire, Andy, additional, Worthy, Doug, additional, Wunch, Debra, additional, Xu, Xiyan, additional, Yoshida, Yukio, additional, Zhang, Bowen, additional, Zhang, Zhen, additional, and Zhu, Qiuan, additional

Published
2017
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28. The global methane budget 2000–2012

Author

Saunois, Marielle, primary, Bousquet, Philippe, additional, Poulter, Ben, additional, Peregon, Anna, additional, Ciais, Philippe, additional, Canadell, Josep G., additional, Dlugokencky, Edward J., additional, Etiope, Giuseppe, additional, Bastviken, David, additional, Houweling, Sander, additional, Janssens-Maenhout, Greet, additional, Tubiello, Francesco N., additional, Castaldi, Simona, additional, Jackson, Robert B., additional, Alexe, Mihai, additional, Arora, Vivek K., additional, Beerling, David J., additional, Bergamaschi, Peter, additional, Blake, Donald R., additional, Brailsford, Gordon, additional, Brovkin, Victor, additional, Bruhwiler, Lori, additional, Crevoisier, Cyril, additional, Crill, Patrick, additional, Covey, Kristofer, additional, Curry, Charles, additional, Frankenberg, Christian, additional, Gedney, Nicola, additional, Höglund-Isaksson, Lena, additional, Ishizawa, Misa, additional, Ito, Akihiko, additional, Joos, Fortunat, additional, Kim, Heon-Sook, additional, Kleinen, Thomas, additional, Krummel, Paul, additional, Lamarque, Jean-François, additional, Langenfelds, Ray, additional, Locatelli, Robin, additional, Machida, Toshinobu, additional, Maksyutov, Shamil, additional, McDonald, Kyle C., additional, Marshall, Julia, additional, Melton, Joe R., additional, Morino, Isamu, additional, Naik, Vaishali, additional, O'Doherty, Simon, additional, Parmentier, Frans-Jan W., additional, Patra, Prabir K., additional, Peng, Changhui, additional, Peng, Shushi, additional, Peters, Glen P., additional, Pison, Isabelle, additional, Prigent, Catherine, additional, Prinn, Ronald, additional, Ramonet, Michel, additional, Riley, William J., additional, Saito, Makoto, additional, Santini, Monia, additional, Schroeder, Ronny, additional, Simpson, Isobel J., additional, Spahni, Renato, additional, Steele, Paul, additional, Takizawa, Atsushi, additional, Thornton, Brett F., additional, Tian, Hanqin, additional, Tohjima, Yasunori, additional, Viovy, Nicolas, additional, Voulgarakis, Apostolos, additional, van Weele, Michiel, additional, van der Werf, Guido R., additional, Weiss, Ray, additional, Wiedinmyer, Christine, additional, Wilton, David J., additional, Wiltshire, Andy, additional, Worthy, Doug, additional, Wunch, Debra, additional, Xu, Xiyan, additional, Yoshida, Yukio, additional, Zhang, Bowen, additional, Zhang, Zhen, additional, and Zhu, Qiuan, additional

Published
2016
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29. Supplementary material to "The Global Methane Budget: 2000–2012"

Author

Saunois, Marielle, primary, Bousquet, Philippe, additional, Poulter, Ben, additional, Peregon, Anna, additional, Ciais, Philippe, additional, Canadell, Josep G., additional, Dlugokencky, Edward J., additional, Etiope, Giuseppe, additional, Bastviken, David, additional, Houweling, Sander, additional, Janssens-Maenhout, Greet, additional, Tubiello, Francesco N., additional, Castaldi, Simona, additional, Jackson, Robert B., additional, Alexe, Mihai, additional, Arora, Vivek K., additional, Beerling, David J., additional, Bergamaschi, Peter, additional, Blake, Donald R., additional, Brailsford, Gordon, additional, Brovkin, Victor, additional, Bruhwiler, Lori, additional, Crevoisier, Cyril, additional, Crill, Patrick, additional, Curry, Charles, additional, Frankenberg, Christian, additional, Gedney, Nicola, additional, Höglund-Isaksson, Lena, additional, Ishizawa, Misa, additional, Ito, Akihiko, additional, Joos, Fortunat, additional, Kim, Heon-Sook, additional, Kleinen, Thomas, additional, Krummel, Paul, additional, Lamarque, Jean-François, additional, Langenfelds, Ray, additional, Locatelli, Robin, additional, Machida, Toshinobu, additional, Maksyutov, Shamil, additional, McDonald, Kyle C., additional, Marshall, Julia, additional, Melton, Joe R., additional, Morino, Isamu, additional, O'Doherty, Simon, additional, Parmentier, Frans-Jan W., additional, Patra, Prabir K., additional, Peng, Changhui, additional, Peng, Shushi, additional, Peters, Glen P., additional, Pison, Isabelle, additional, Prigent, Catherine, additional, Prinn, Ronald, additional, Ramonet, Michel, additional, Riley, William J., additional, Saito, Makoto, additional, Schroeder, Ronny, additional, Simpson, Isobel J., additional, Spahni, Renato, additional, Steele, Paul, additional, Takizawa, Atsushi, additional, Thornton, Brett F., additional, Tian, Hanqin, additional, Tohjima, Yasunori, additional, Viovy, Nicolas, additional, Voulgarakis, Apostolos, additional, van Weele, Michiel, additional, van der Werf, Guido, additional, Weiss, Ray, additional, Wiedinmyer, Christine, additional, Wilton, David J., additional, Wiltshire, Andy, additional, Worthy, Doug, additional, Wunch, Debra B., additional, Xu, Xiyan, additional, Yoshida, Yukio, additional, Zhang, Bowen, additional, Zhang, Zhen, additional, and Zhu, Qiuan, additional

Published
2016
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30. UPGRADED STAND FOR TESTING OF HYDRAULICALLY ASSISTED STEERING BOXES FOR CARS.

Author

RĂDOI, Radu - Iulian, ANGHELACHE, Gabriel, HRISTEA, Alexandru, ALEXE, Mihai, and TUDOR, Bogdan

Subjects
POWER steering, TRANSDUCERS, HYDRAULIC control systems
Abstract

An economic partner wanted to improve a test bench for testing hydraulic assisted power steering and pumps for passenger cars and vans. Modernization of existing technology through computerization will reduce testing time and diminish the consumption of electricity, but also eliminate any human error which may occur in the verification process of repair; in this way there is the certainty of a subassembly that provides full security to the user. For upgrading, a number of subassemblies have been installed on the stand that allow the stand to be interfaced with a computer. The subassemblies consist of a series of transducers, electronic block, a data acquisition board and a drive system for the steering box valve. The paper presents the construction and functioning of the necessary subassemblies for stand upgrading and their characteristics. [ABSTRACT FROM AUTHOR]

Published
2017

31. EQUIPMENT AND TECHNOLOGIES FOR OBTAINING BIOGAS AND BIOCHAR FROM BIOMASS.

Author

Matache, Gabriela, Sovaila, Gheorghe, Radoi, Radu-Iulian, Hristea, Alexandru-Mihai, and Alexe, Mihai Adrian

Subjects
BIOGAS, BIOCHAR, BIOMASS, BIOMASS gasification, HEAT exchangers
Abstract

This article presents the results, obtained from testing an equipment used to obtain biogas and biochar from biomass, developed and manufactured based on a patent developed on a research and development project by the IHP staff. A modern way in order to produce ecological and cheap thermal energy is to utilize biomass through micro-gasification process called TLUD (Top-Lit UpDraft) with high conversion efficiencies and emissions of CO and low PM2,5. A new way for producing energy from biomass is the CHAB concept (Combined Heat and Biochar) which produces both heat and biochar (BC) with negative balance of CO2. The CHAB concept applies in those agricultural sectors which are large consumers of heat: greenhouses, drying, technological spaces. For heating greenhouses was designed and produced a hot air generator which consists from two major subsystems: the generator with gas-producing TLUD and air heater with heat exchanger flue gas/hot air. Acceptability for long-term use of gasification technology and the CHAB and the penetration on the market depend on its economic, ecologic and technical performances of the gasification and production of biochar installations, efficiency and safety of installations that use the produced gas as well as the tolerances for the characteristics of gasification biomass. Tests of the equipment shows the characteristics obtained from the proposed version and done by the research staff so that the operational system had to be safe, reliable, meet the environmental standards and to be economically viable. This tests whore conducted based on a methodology presented during this paper. [ABSTRACT FROM AUTHOR]

Published
2017
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33. A fully discrete framework for the adaptive solution of inverse problems

Author

Alexe, Mihai, Sandu, Adrian, and Computer Science

Subjects
Numerical analysis
Abstract

We investigate and contrast the differences between the discretize-then-differentiate and differentiate-then-discretize approaches to the numerical solution of parameter estimation problems. The former approach is attractive in practice due to the use of automatic differentiation for the generation of the dual and optimality equations in the first-order KKT system. The latter strategy is more versatile, in that it allows one to formulate efficient mesh-independent algorithms over suitably chosen function spaces. However, it is significantly more difficult to implement, since automatic code generation is no longer an option. The starting point is a classical elliptic inverse problem. An a priori error analysis for the discrete optimality equation shows consistency and stability are not inherited automatically from the primal discretization. Similar to the concept of dual consistency, We introduce the concept of optimality consistency. However, the convergence properties can be restored through suitable consistent modifications of the target functional. Numerical tests confirm the theoretical convergence order for the optimal solution. We then derive a posteriori error estimates for the infinite dimensional optimal solution error, through a suitably chosen error functional. This estimates are constructed using second order derivative information for the target functional. For computational efficiency, the Hessian is replaced by a low order BFGS approximation. The efficiency of the error estimator is confirmed by a numerical experiment with multigrid optimization.

Published
2012

34. Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems

Author

Alexe, Mihai, Computer Science, Sandu, Adrian, Ribbens, Calvin J., Cao, Yang, de Sturler, Eric, and Borggaard, Jeffrey T.

Subjects
Inverse problems, Automatic Differentiation, Adjoint Method, Adaptive Mesh Refinement
Abstract

Adaptivity in both space and time has become the norm for solving problems modeled by partial differential equations. The size of the discretized problem makes uniformly refined grids computationally prohibitive. Adaptive refinement of meshes and time steps allows to capture the phenomena of interest while keeping the cost of a simulation tractable on the current hardware. Many fields in science and engineering require the solution of inverse problems where parameters for a given model are estimated based on available measurement information. In contrast to forward (regular) simulations, inverse problems have not extensively benefited from the adaptive solver technology. Previous research in inverse problems has focused mainly on the continuous approach to calculate sensitivities, and has typically employed fixed time and space meshes in the solution process. Inverse problem solvers that make exclusive use of uniform or static meshes avoid complications such as the differentiation of mesh motion equations, or inconsistencies in the sensitivity equations between subdomains with different refinement levels. However, this comes at the cost of low computational efficiency. More efficient computations are possible through judicious use of adaptive mesh refinement, adaptive time steps, and the discrete adjoint method. This dissertation develops a complete framework for fully discrete adjoint sensitivity analysis and inverse problem solutions, in the context of time dependent, adaptive mesh, and adaptive step models. The discrete framework addresses all the necessary ingredients of a state–of–the–art adaptive inverse solution algorithm: adaptive mesh and time step refinement, solution grid transfer operators, a priori and a posteriori error analysis and estimation, and discrete adjoints for sensitivity analysis of flux–limited numerical algorithms. Ph. D.

Published
2011

35. Space-time adaptive solution of inverse problems with the discrete adjoint method

Author

Alexe, Mihai, Sandu, Adrian, and Computer Science

Subjects
MathematicsofComputing_NUMERICALANALYSIS, Numerical analysis
Abstract

Adaptivity in both space and time has become the norm for solving problems modeled by partial differential equations. The size of the discretized problem makes uniformly refined grids computationally prohibitive. Adaptive refinement of meshes and time steps allows to capture the phenomena of interest while keeping the cost of a simulation tractable on the current hardware. Many fields in science and engineering require the solution of inverse problems where parameters for a given model are estimated based on available measurement information. In contrast to forward (regular) simulations, inverse problems have not extensively benefited from the adaptive solver technology. Previous research in inverse problems has focused mainly on the continuous approach to calculate sensitivities, and has typically employed fixed time and space meshes in the solution process. Inverse problem solvers that make exclusive use of uniform or static meshes avoid complications such as the differentiation of mesh motion equations, or inconsistencies in the sensitivity equations between subdomains with different refinement levels. However, this comes at the cost of low computational efficiency. More efficient computations are possible through judicious use of adaptive mesh refinement, adaptive time steps, and the discrete adjoint method. This paper develops a framework for the construction and analysis of discrete adjoint sensitivities in the context of time dependent, adaptive grid, adaptive step models. Discrete adjoints are attractive in practice since they can be generated with low effort using automatic differentiation. However, this approach brings several important challenges. The adjoint of the forward numerical scheme may be inconsistent with the continuous adjoint equations. A reduction in accuracy of the discrete adjoint sensitivities may appear due to the intergrid transfer operators. Moreover, the optimization algorithm may need to accommodate state and gradient vectors whose dimensions change between iterations. This work shows that several of these potential issues can be avoided for the discontinuous Galerkin (DG) method. The adjoint model development is considerably simplified by decoupling the adaptive mesh refinement mechanism from the forward model solver, and by selectively applying automatic differentiation on individual algorithms. In forward models discontinuous Galerkin discretizations can efficiently handle high orders of accuracy, $h/p$-refinement, and parallel computation. The analysis reveals that this approach, paired with Runge Kutta time stepping, is well suited for the adaptive solutions of inverse problems. The usefulness of discrete discontinuous Galerkin adjoints is illustrated on a two-dimensional adaptive data assimilation problem.

Published
2010

36. A Hybrid Approach to Estimating Error Covariances in Variational Data Assimilation

Author

Cheng, Haiyan, Jardak, Mohamed, Alexe, Mihai, Sandu, Adrian, and Computer Science

Subjects
Mathematical software, Numerical analysis
Abstract

Data Assimilation (DA) involves the combination of observational data with the underlying dynamical principles governing the system under observation. In this work we combine the advantages of the two prominent advanced data assimilation systems, the 4D-Var and the ensemble methods. The proposed method consists of identifying the subspace spanned by the major 4D-Var error reduction directions. These directions are then removed from the background covariance through a Galerkin-type projection. This generates an updated error covariance information at both end points of an assimilation window. The error covariance information is updated between assimilation windows to capture the ``error of the day''. Numerical results using our new hybrid approach on a nonlinear model demonstrate how the background covariance matrix leads to an error covariance update that improves the 4D-Var DA results.

Published
2009

37. PRODUCTION OF BIOCHAR IN CHAB CONCEPT FOR REDUCING CO2 CONCENTRATIONS IN THE ENVIRONMENT AND SUSTAINABLE AGRICULTURAL DEVELOPMENT.

Author

Alexe, Mihai and Dima, Alexandra

Subjects
*BIOCHAR, *BIOMASS gasification, *ATMOSPHERIC carbon dioxide, ENVIRONMENTAL aspects
Abstract

In this paper is presented the synergistic concept CHAB (Combined Heat and Biochar production) and production of biochar from biomass by TLUD gasification process. Micro-gasification units using TLUD process are easy to operate, safe, simple and relatively cheap. Quality of biochar is easily controlled and defined in current standards, unlike other artisanal devices where there is no control over the thermal regime, resulting in uncertain quality of biochar. TLUD micro-gasification units can be used in a wide range of practical applications: heating greenhouses, dryers, food preparation, home heating, water heating, etc. Biochar resulting from TLUD micro-gasification can be used as soil amendment in agriculture, proportions of 5-15%. The carbon from biochar remains sequestered in agricultural soil increasing the soil fertility by 10-30 %. Applying the CHAB concept fully exploits the chemically and energetic potential of biomass, helping to increase energy independence in agriculture with a negative carbon balance, and therefore the concentration of CO2 from the atmosphere. [ABSTRACT FROM AUTHOR]

Published
2016

38. SMART ROOT CUTTING MACHINE USED IN BIOTECHNOLOGY FOR DIRECTING THE GROWTH OF ROOT SYSTEM IN ORCHARDS.

Author

Angel, Sava, Matache, Gabriela, Sovaiala, Gheorghe, Tanasescu, Nicolae, Alexe, Mihai, Pavel, Ioan, and Marinescu, Alexandru

Subjects
HYDRAULIC control systems, HORTICULTURE, AGRICULTURAL biotechnology
Abstract

Worldwide the trends of evolution in the field of horticulture are the easy maintenance for plantations, with as small volume of work, control of nutrients, water treatment and maintenance of trees. Thereby have appeared intensive and superintensive orchards. In order to make easier maintenance operation and harvesting, the trees must be small sized, planted in rows with a high density. Irrigation is done with fertilizers on a certain area, along of the root system. The agricultural machines have developed properly with the appearance of new specific machines, among which are controlled machines for establishing load of inflorescence of fruit and controlled cutting machine for root system. Quality of the works done by cars must comply with the technology fitted in the limits imposed by the orchard technologist. In order to obtain quality work it is necessary to have high precision machines, with the possibility to precise control of the working tool. This can be done using distance, horizontality, and angle sensors that send the position of the working tool to an automatic control system, than through a hydraulic system it corrects the tool position, keeping it in a field of required working technology. This automatic control, via the hydraulic system, corrects the tool position, keeping it in a field of the required working technology. [ABSTRACT FROM AUTHOR]

Published
2016

39. Denserks: Fortran Sensitivity Solvers Using Continuous, Explicit Runge-kutta Schemes

Author

Alexe, Mihai, Sandu, Adrian, and Computer Science

Subjects
MathematicsofComputing_NUMERICALANALYSIS, Mathematical software
Abstract

DENSERKS is a Fortran sensitivity equation solver package designed for integrating models whose evolution can be described by ordinary differential equations (ODEs). A salient feature of DENSERKS is its support for both forward and adjoint sensitivity analyses, with built-in integrators for both first and second order continuous adjoint models. The software implements explicit Runge-Kutta methods with adaptive timestepping and high-order dense output schemes for the forward and the tangent linear model trajectory interpolation. Implementations of six Runge-Kutta methods are provided, with orders of accuracy ranging from two to eight. This makes DENSERKS suitable for a wide range of practical applications. The use of dense output, a novel approach in adjoint sensitivity analysis solvers, allows for a high-order cost-effective interpolation. This is a necessary feature when solving adjoints of nonlinear systems using highly accurate Runge-Kutta methods (order five and above). To minimize memory requirements and make long-time integrations computationally efficient, DENSERKS implements a two-level checkpointing mechanism. The code is tested on a selection of problems illustrating first and second order sensitivity analysis with respect to initial model conditions. The resulting derivative information is also used in a gradient-based optimization algorithm to minimize cost functionals dependent on a given set of model parameters.

Published
2007

40. Electronic Control Module for Test Stand on the Production Line of Hydraulic Cylinders and Rotary Hydraulic Motors.

Author

RĂDOI, Radu, HRISTEA, Alexandru, TUDOR, Bogdan, and ALEXE, Mihai

Subjects
HYDRAULIC cylinders, HYDRAULIC motors, ELECTRONIC control, MICROCONTROLLERS, DETECTORS
Abstract

In order to ensure the quality of the products delivered at the exit from the production lines there are used functionality testing stands which are located at the exit of the line. The functionality of the products is verified with the help of specialized stands. The testing stand can be controlled with dedicated electronic modules which ensure the logic of operation, or with PLC's. This paper will present an electronic module from a hydraulic motor testing stand, also provided with an operating console, which communicates serially with the control module. [ABSTRACT FROM AUTHOR]

Published
2017

41. A fully discrete framework for the adaptive solution of inverse problems

Author

Computer Science, Alexe, Mihai, Sandu, Adrian, Computer Science, Alexe, Mihai, and Sandu, Adrian

Abstract

We investigate and contrast the differences between the discretize-then-differentiate and differentiate-then-discretize approaches to the numerical solution of parameter estimation problems. The former approach is attractive in practice due to the use of automatic differentiation for the generation of the dual and optimality equations in the first-order KKT system. The latter strategy is more versatile, in that it allows one to formulate efficient mesh-independent algorithms over suitably chosen function spaces. However, it is significantly more difficult to implement, since automatic code generation is no longer an option. The starting point is a classical elliptic inverse problem. An a priori error analysis for the discrete optimality equation shows consistency and stability are not inherited automatically from the primal discretization. Similar to the concept of dual consistency, We introduce the concept of optimality consistency. However, the convergence properties can be restored through suitable consistent modifications of the target functional. Numerical tests confirm the theoretical convergence order for the optimal solution. We then derive a posteriori error estimates for the infinite dimensional optimal solution error, through a suitably chosen error functional. This estimates are constructed using second order derivative information for the target functional. For computational efficiency, the Hessian is replaced by a low order BFGS approximation. The efficiency of the error estimator is confirmed by a numerical experiment with multigrid optimization.

Published
2012

42. Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems

Author

Computer Science, Sandu, Adrian, Ribbens, Calvin J., Cao, Yang, de Sturler, Eric, Borggaard, Jeffrey T., Alexe, Mihai, Computer Science, Sandu, Adrian, Ribbens, Calvin J., Cao, Yang, de Sturler, Eric, Borggaard, Jeffrey T., and Alexe, Mihai

Abstract

Adaptivity in both space and time has become the norm for solving problems modeled by partial differential equations. The size of the discretized problem makes uniformly refined grids computationally prohibitive. Adaptive refinement of meshes and time steps allows to capture the phenomena of interest while keeping the cost of a simulation tractable on the current hardware. Many fields in science and engineering require the solution of inverse problems where parameters for a given model are estimated based on available measurement information. In contrast to forward (regular) simulations, inverse problems have not extensively benefited from the adaptive solver technology. Previous research in inverse problems has focused mainly on the continuous approach to calculate sensitivities, and has typically employed fixed time and space meshes in the solution process. Inverse problem solvers that make exclusive use of uniform or static meshes avoid complications such as the differentiation of mesh motion equations, or inconsistencies in the sensitivity equations between subdomains with different refinement levels. However, this comes at the cost of low computational efficiency. More efficient computations are possible through judicious use of adaptive mesh refinement, adaptive time steps, and the discrete adjoint method. This dissertation develops a complete framework for fully discrete adjoint sensitivity analysis and inverse problem solutions, in the context of time dependent, adaptive mesh, and adaptive step models. The discrete framework addresses all the necessary ingredients of a state–of–the–art adaptive inverse solution algorithm: adaptive mesh and time step refinement, solution grid transfer operators, a priori and a posteriori error analysis and estimation, and discrete adjoints for sensitivity analysis of flux–limited numerical algorithms.

Published
2011

43. Second order adjoints for solving PDE-constrained optimization problems

Author

Computer Science, Cioaca, Alexandru, Alexe, Mihai, Sandu, Adrian, Computer Science, Cioaca, Alexandru, Alexe, Mihai, and Sandu, Adrian

Abstract

Inverse problems are of utmost importance in many fields of science and engineering. In the variational approach inverse problems are formulated as PDE-constrained optimization problems, where the optimal estimate of the uncertain parameters is the minimizer of a certain cost functional subject to the constraints posed by the model equations. The numerical solution of such optimization problems requires the computation of derivatives of the model output with respect to model parameters. The first order derivatives of a cost functional (defined on the model output) with respect to a large number of model parameters can be calculated efficiently through first order adjoint sensitivity analysis. Second order adjoint models give second derivative information in the form of matrix-vector products between the Hessian of the cost functional and user defined vectors. Traditionally, the construction of second order derivatives for large scale models has been considered too costly. Consequently, data assimilation applications employ optimization algorithms that use only first order derivative information, like nonlinear conjugate gradients and quasi-Newton methods. In this paper we discuss the mathematical foundations of second order adjoint sensitivity analysis and show that it provides an efficient approach to obtain Hessian-vector products. We study the benefits of using of second order information in the numerical optimization process for data assimilation applications. The numerical studies are performed in a twin experiment setting with a two-dimensional shallow water model. Different scenarios are considered with different discretization approaches, observation sets, and noise levels. Optimization algorithms that employ second order derivatives are tested against widely used methods that require only first order derivatives. Conclusions are drawn regarding the potential benefits and the limitations of using high-order information in large scale data assimilation proble

Published
2010

44. Space-time adaptive solution of inverse problems with the discrete adjoint method

Author

Computer Science, Alexe, Mihai, Sandu, Adrian, Computer Science, Alexe, Mihai, and Sandu, Adrian

Abstract

Adaptivity in both space and time has become the norm for solving problems modeled by partial differential equations. The size of the discretized problem makes uniformly refined grids computationally prohibitive. Adaptive refinement of meshes and time steps allows to capture the phenomena of interest while keeping the cost of a simulation tractable on the current hardware. Many fields in science and engineering require the solution of inverse problems where parameters for a given model are estimated based on available measurement information. In contrast to forward (regular) simulations, inverse problems have not extensively benefited from the adaptive solver technology. Previous research in inverse problems has focused mainly on the continuous approach to calculate sensitivities, and has typically employed fixed time and space meshes in the solution process. Inverse problem solvers that make exclusive use of uniform or static meshes avoid complications such as the differentiation of mesh motion equations, or inconsistencies in the sensitivity equations between subdomains with different refinement levels. However, this comes at the cost of low computational efficiency. More efficient computations are possible through judicious use of adaptive mesh refinement, adaptive time steps, and the discrete adjoint method. This paper develops a framework for the construction and analysis of discrete adjoint sensitivities in the context of time dependent, adaptive grid, adaptive step models. Discrete adjoints are attractive in practice since they can be generated with low effort using automatic differentiation. However, this approach brings several important challenges. The adjoint of the forward numerical scheme may be inconsistent with the continuous adjoint equations. A reduction in accuracy of the discrete adjoint sensitivities may appear due to the intergrid transfer operators. Moreover, the optimization algorithm may need to accommodate state and gradient vectors whose di

Published
2010

45. A Hybrid Approach to Estimating Error Covariances in Variational Data Assimilation

Author

Computer Science, Cheng, Haiyan, Jardak, Mohamed, Alexe, Mihai, Sandu, Adrian, Computer Science, Cheng, Haiyan, Jardak, Mohamed, Alexe, Mihai, and Sandu, Adrian

Abstract

Data Assimilation (DA) involves the combination of observational data with the underlying dynamical principles governing the system under observation. In this work we combine the advantages of the two prominent advanced data assimilation systems, the 4D-Var and the ensemble methods. The proposed method consists of identifying the subspace spanned by the major 4D-Var error reduction directions. These directions are then removed from the background covariance through a Galerkin-type projection. This generates an updated error covariance information at both end points of an assimilation window. The error covariance information is updated between assimilation windows to capture the ``error of the day''. Numerical results using our new hybrid approach on a nonlinear model demonstrate how the background covariance matrix leads to an error covariance update that improves the 4D-Var DA results.

Published
2009

46. Acquired benign tracheo‑oesophageal fistula secondary to oesophageal stenting for post lye ingestion stenosis: A case report.

Author

Bolca, Ciprian, Bobocea, Andrei, Cosoveanu, George, Alexe, Mihai, Cadar, Genoveva, Balescu, Irina, Bacalbasa, Nicolae, Gherghiceanu, Florentina, Furtunescu, Florentina, Radavoi, Daniel, Paleru, Cristian, Miron, Adrian, and Cordos, Ioan

Subjects
HIGH-frequency ventilation (Therapy), ESOPHAGEAL fistula, ASPIRATION pneumonia, STENOSIS, INGESTION
Abstract

Acquired benign trachea-oesophageal fistula is a rare benign pathological entity with varying aetiologies that most often occurs post-intubation. This case report presents the case of a female patient, 31 years old, admitted to the emergency room with sepsis syndrome following bilateral aspiration pneumonia caused by a large trachea-oesophageal fistula. The fistula was the result of intra-tracheal migration of an oesophageal stent placed for post lye ingestion stenosis. Esophageal diversion and partial resection with oesophageal patch to repair the tracheal defect, under general anaesthesia with ventilation using rigid bronchoscopy and high frequency jet ventilation (HFJV), followed at a later date by esophageal replacement with colic graft were the procedures performed with a view to curing the patient. In conclusion, complex cases always require a tailored approach. It is important to note that HFJV may be applied for a longer period of time and the oesophagus can be used as patch for the posterior tracheal wall in selected cases. Staged surgery is also an option when the patients' poor health status does not permit major surgery. [ABSTRACT FROM AUTHOR]

Published
2023

47. On the discrete adjoints of adaptive time stepping algorithms

Author

Computer Science, Alexe, Mihai, Sandu, Adrian, Computer Science, Alexe, Mihai, and Sandu, Adrian

Abstract

We investigate the behavior of adaptive time stepping numerical algorithms under the reverse mode of automatic differentiation (AD). By differentiating the time step controller and the error estimator of the original algorithm, reverse mode AD generates spurious adjoint derivatives of the time steps. The resulting discrete adjoint models become inconsistent with the adjoint ODE, and yield incorrect derivatives. To regain consistency, one has to cancel out the contributions of the non-physical derivatives in the discrete adjoint model. We demonstrate that the discrete adjoint models of one-step, explicit adaptive algorithms, such as the Runge--Kutta schemes, can be made consistent with their continuous analogs using simple code modifications. Furthermore, we extend the analysis to cover second order adjoint models derived through an extra forward-mode differentiation of the discrete adjoint code. Two numerical examples support the mathematical derivations.

Published
2008

48. Denserks: Fortran Sensitivity Solvers Using Continuous, Explicit Runge-kutta Schemes

Author

Computer Science, Alexe, Mihai, Sandu, Adrian, Computer Science, Alexe, Mihai, and Sandu, Adrian

Abstract

DENSERKS is a Fortran sensitivity equation solver package designed for integrating models whose evolution can be described by ordinary differential equations (ODEs). A salient feature of DENSERKS is its support for both forward and adjoint sensitivity analyses, with built-in integrators for both first and second order continuous adjoint models. The software implements explicit Runge-Kutta methods with adaptive timestepping and high-order dense output schemes for the forward and the tangent linear model trajectory interpolation. Implementations of six Runge-Kutta methods are provided, with orders of accuracy ranging from two to eight. This makes DENSERKS suitable for a wide range of practical applications. The use of dense output, a novel approach in adjoint sensitivity analysis solvers, allows for a high-order cost-effective interpolation. This is a necessary feature when solving adjoints of nonlinear systems using highly accurate Runge-Kutta methods (order five and above). To minimize memory requirements and make long-time integrations computationally efficient, DENSERKS implements a two-level checkpointing mechanism. The code is tested on a selection of problems illustrating first and second order sensitivity analysis with respect to initial model conditions. The resulting derivative information is also used in a gradient-based optimization algorithm to minimize cost functionals dependent on a given set of model parameters.

Published
2007

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