Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5695-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5695-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
14 Sep 2021
Model description paper |
| 14 Sep 2021
| 14 Sep 2021
NDCmitiQ v1.0.0: a tool to quantify and analyse greenhouse gas mitigation targets
Annika Günther
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
Johannes Gütschow
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, 14412 Potsdam, Germany
Mairi Louise Jeffery
NewClimate Institute, Schönhauser Allee 10–11, 10119 Berlin, Germany
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Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
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This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Francesco N. Tubiello, Kevin Karl, Alessandro Flammini, Johannes Gütschow, Griffiths Obli-Laryea, Giulia Conchedda, Xueyao Pan, Sally Yue Qi, Hörn Halldórudóttir Heiðarsdóttir, Nathan Wanner, Roberta Quadrelli, Leonardo Rocha Souza, Philippe Benoit, Matthew Hayek, David Sandalow, Erik Mencos Contreras, Cynthia Rosenzweig, Jose Rosero Moncayo, Piero Conforti, and Maximo Torero
Earth Syst. Sci. Data, 14, 1795–1809, https://doi.org/10.5194/essd-14-1795-2022, https://doi.org/10.5194/essd-14-1795-2022, 2022
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The paper presents results from the new FAOSTAT database on food system emissions, covering all countries over the time series 1990–2019. Results indicate and further clarify – updated to 2019 – the relevance of emissions from crop and livestock production processes within the farm gate; from conversion of natural ecosystems to agriculture, such as deforestation and peat degradation; and from use of fossil fuels for energy and other industrial processes along food supply chains.
Johannes Gütschow, M. Louise Jeffery, Annika Günther, and Malte Meinshausen
Earth Syst. Sci. Data, 13, 1005–1040, https://doi.org/10.5194/essd-13-1005-2021, https://doi.org/10.5194/essd-13-1005-2021, 2021
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Climate policy analysis needs scenarios of future greenhouse gas emission to assess countries' emission targets and current trends. The models generating these scenarios work on a regional resolution. Scenarios are often made available only on a very coarse regional resolution. In this paper we use per country projections of gross domestic product (GDP) from the Shared Socio-Economic Pathways (SSPs) to derive country-level data from published regional emission scenarios.
M. Louise Jeffery, Johannes Gütschow, Robert Gieseke, and Ronja Gebel
Earth Syst. Sci. Data, 10, 1427–1438, https://doi.org/10.5194/essd-10-1427-2018, https://doi.org/10.5194/essd-10-1427-2018, 2018
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Developed countries are required to report detailed greenhouse gas emissions data to the UN on an annual basis. The reporting tables are complex, do not fit well with existing hierarchical reporting guidelines, and are not machine-readable. We present a processed version of the reported data in a consistent hierarchy, and in a format that is machine-readable and easy-to-use. The emissions data are also aggregated into
basketsof gases using global warming equivalency metrics from IPCC reports.
Johannes Gütschow, M. Louise Jeffery, Robert Gieseke, Ronja Gebel, David Stevens, Mario Krapp, and Marcia Rocha
Earth Syst. Sci. Data, 8, 571–603, https://doi.org/10.5194/essd-8-571-2016, https://doi.org/10.5194/essd-8-571-2016, 2016
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This paper provides the methodology for the creation of historical country-resolved time series of greenhouse gas emissions from different datasets which are individually incomplete in terms of years, gases, and/or countries. The combination of datasets is carried out using the PRIMAP model (www.primap.org). The resulting time series can be viewed interactively on www.pik-potsdam.de/primap-live. It will be used for climate policy analysis, e.g. the historical responsibility for climate change.
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Stefania Renna, Francesco Granella, Lara Aleluia Reis, and Paulina Estela Schulz-Antipa
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Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
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Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
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Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
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The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
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Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
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This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
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Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
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It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
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Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
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The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
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The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
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This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
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This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Ane Lopez de Gamiz-Zearra, Cecilie Hansen, Xavier Corrales, Iñaki Quincoces, Izaskun Preciado, and Eider Andonegi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1368, https://doi.org/10.5194/egusphere-2023-1368, 2023
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This paper describes the development of the first calibrated end-to-end Atlantis model for the Bay of Biscay that will help us improve the comprehension of the spatial functioning of the Bay of Biscay ecosystem and help establishing management measures of human activities. Our results highlighted the importance of lower trophic levels to the pelagic system and demonstrate the importance of having accurate and precise data for biological processes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
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We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
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Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
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To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
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A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
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The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Cited articles
Bauer, N., Calvin, K., Emmerling, J., Fricko, O., Fujimori, S., Hilaire, J.,
Eom, J., Krey, V., Kriegler, E., Mouratiadou, I., de Boer, H. S., van den
Berg, M., Carrara, S., Daioglou, V., Drouet, L., Edmonds, J. E., Gernaat, D.,
Havlik, P., Johnson, N., Klein, D., Kyle, P., Marangoni, G., Masui, T.,
Pietzcker, R. C., Strubegger, M., Wise, M., Riahi, K., and van Vuuren, D. P.:
Shared Socio-Economic Pathways of the Energy Sector – Quantifying
the Narratives, Global Environ. Change, 42, 316–330,
https://doi.org/10.1016/j.gloenvcha.2016.07.006, 2017. a
Benveniste, H., Boucher, O., Guivarch, C., Treut, H. L., and Criqui, P.:
Impacts of nationally determined contributions on 2030 global greenhouse gas
emissions: uncertainty analysis and distribution of emissions, Environ.
Res. Lett., 13, 014022, https://doi.org/10.1088/1748-9326/aaa0b9, 2018. a, b, c, d, e, f, g, h
Carey, E. V., Sala, A., Keane, R., and Callaway, R. M.: Are old forests
underestimated as global carbon sinks?, Glob. Change Biol., 7, 339–344,
https://doi.org/10.1046/j.1365-2486.2001.00418.x, 2001. a
CAT: Climate Action Tracker: Australia: Assumptions: Pledge,
available at: https://climateactiontracker.org/countries/australia/assumptions/ (last access: 1 June 2020), 2019a. a
CAT: Climate Action Tracker: Brazil: Assumptions: NDC,
https://climateactiontracker.org/countries/brazil/assumptions/ (last access: 1 June 2020), 2019b. a
CAT: Climate Action Tracker: India: Assumptions (update from 02 Dec 2019),
available at: https://climateactiontracker.org/countries/india/assumptions/ (last access: 7 May 2020), 2019c. a
CAT: Climate Action Tracker: Temperatures: Addressing global warming,
available at: https://climateactiontracker.org/global/temperatures/, last access: 5 June 2020a. a
Climate Equity Reference: Climate Equity Reference Calculator, available at:
https://calculator.climateequityreference.org/ (last access: 1 June 2020), 2018. a
Climate Watch: Climate Watch – Data for Climate Action, available at:
https://www.climatewatchdata.org/, last access: 7 May 2020b. a
Crespo Cuaresma, J.: Income projections for climate change research: A
framework based on human capital dynamics, Global Environ. Change, 42,
226–236, https://doi.org/10.1016/j.gloenvcha.2015.02.012, 2017. a
Dellink, R., Chateau, J., Lanzi, E., and Magné, B.: Long-term economic growth
projections in the Shared Socioeconomic Pathways, Global Environ.
Change, 42, 200–214, https://doi.org/10.1016/j.gloenvcha.2015.06.004, 2017. a
den Elzen, M., Kuramochi, T., Höhne, N., Cantzler, J., Esmeijer, K., Fekete, H., Fransen, T., Keramidas, K., Roelfsema, M., Sha, F., van Soest, H., and
Vandyck, T.: Are the G20 economies making enough progress to meet their NDC
targets?, Energy Policy, 126, 238–250,
https://doi.org/10.1016/j.enpol.2018.11.027, 2019. a
FAO: FAOSTAT Database, available at:
http://www.fao.org/faostat/en/#data, last access: 14 October 2019. a
Forsell, N., Turkovska, O., Gusti, M., Obersteiner, M., den Elzen, M., and
Havlik, P.: Assessing the INDCs' land use, land use change, and forest
emission projections, Carbon Balance and Management, 11, 26,
https://doi.org/10.1186/s13021-016-0068-3, 2016. a
Geiges, A., Parra, P. Y., Fyson, C., Günther, A., Hare, B., Schaeffer, M., and Hutfilter, U. F.: How can Paris Agreement commitments be improved now to close the gap to 1.5∘C: Global long-term temperature outcomes of
incremental versus transformational ambition scenarios for NDCs updates by
2020, available at: https://climateanalytics.org/media/ndc_closing_the_gap_to_1p5c.pdf (last access: 15 June 2020),
2019. a
Gombar, V.: India 'Walking the Walk' on Climate: Q&A, available at:
https://about.bnef.com/blog/india-walking-the-walk-on-climate-qa/?utm_campaign=Carbon Brief Daily Briefing&utm_content=20201015&utm_medium=email&utm_source=Revue newsletter,
last access: 14 October 2020. a
Graichen, J., Blank, D., Graichen, V., Harthan, R., and Herold, A.: Accounting of Nationally Determined Contributions: Guidance for the Establishment of an Accounting for NDCs for absolute or relative mitigation targets with a
baseline, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ)
GmbH, available at: https://www.transparency-partnership.net/system/files/document/Guidance Accounting NDC_eng.pdf (last access: 3 May 2020),
2018. a
Günther, A., Gütschow, J., and Jeffery, M. L.: NDCmitiQ: a tool to
quantify and analyse GHG mitigation targets (Version v1.0.2), Zenodo,
https://doi.org/10.5281/zenodo.5113987, 2021. a
Gütschow, J.: The PRIMAP-Hist Socio-Eco Historical GDP and Population Time Series (1850–2017) (v2.1), GFZ Data Services, https://doi.org/10.5880/PIK.2019.019, 2019. a, b
Gütschow, J., Jeffery, M. L., Gieseke, R., Gebel, R., Stevens, D., Krapp, M., and Rocha, M.: The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571–603, https://doi.org/10.5194/essd-8-571-2016, 2016. a, b, c
Gütschow, J., Jeffery, M. L., Schaeffer, M., and Hare, B.: Extending
Near-Term Emissions Scenarios to Assess Warming Implications of Paris
Agreement NDCs, Earth's Future, 6, 1242–1259,
https://doi.org/10.1002/2017ef000781, 2018. a
Gütschow, J., Jeffery, M. L., and Annika, G.: PRIMAP-crf: UNFCCC CRF data in IPCC categories (PRIMAP-crf-2019-v2), Zenodo, https://doi.org/10.5281/zenodo.3775575,
2020a. a
Gütschow, J., Jeffery, M. L., Günther, A., and Meinshausen, M.: Country Resolved Combined Emission and Socio-Economic Pathways Based on the RCP and SSP Scenarios – Dataset, Zenodo https://doi.org/10.5281/zenodo.3638137, 2020b. a, b, c, d
Gütschow, J., Jeffery, M. L., Günther, A., and Meinshausen, M.: Country-resolved combined emission and socio-economic pathways based on the Representative Concentration Pathway (RCP) and Shared Socio-Economic Pathway (SSP) scenarios, Earth Syst. Sci. Data, 13, 1005–1040, https://doi.org/10.5194/essd-13-1005-2021, 2021. a, b, c
Hargita, Y. and Rüter, S.: Analysis of the land use sector in INDCs of
relevant Non-Annex I parties, available at:
https://literatur.thuenen.de/digbib_extern/dn055903.pdf (last access: 1 February 2020), 2015. a
Hausfather, Z. and Peters, G. P.: RCP8.5 is a problematic scenario for
near-term emissions, P. Natl. Acad. Sci. USA, 117, 27791–27792,
https://doi.org/10.1073/pnas.2017124117, 2020. a
Hegerl, G. C., Zwiers, F. W., Braconnot, P., Gillett, N. P., Luo, Y., Orsini,
J. A. M., Nicholls, N., Penner, J. E., and Scott, P. A.: Understanding and
Attributing Climate Change, Cambridge University Press, Cambridge, UK, New York, NY, USA, 2007. a
IPCC: Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, available at: https://www.ipcc.ch/report/ar4/wg1/ (last access: 21 July 2020), 2007. a
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Group I,
II and III to the Fifth Assessment Report of the Interngovernmental Panel on
Climate Change, IPCC, Geneva, Switzerland, available at:
https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf (last access: 27 February 2020),
2014. a, b
IPCC: Global warming of 1.5 ∘C. An IPCC Special Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty, Interngovernmental Panel on Climate Change, 2018a. a, b
IPCC: Summary for Policymakers, in: Global warming of 1.5 ∘C, An IPCC Special
Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels
and related global greenhouse gas emission pathways, in the context of
strengthening the global response to the threat of climate change,
sustainable development, and efforts to eradicate poverty, edited by: Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J.,
Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and Waterfield, T., Interngovernmental Panel on Climate Change, available at: https://www.ipcc.ch/sr15/chapter/spm/ (last access: 5 August 2020), 2018b. a
IPCC: Climate Change and Land: an IPCC special report on climate change,
desertification, land degradation, sustainable land management, food
security, and greenhouse gas fluxes in terrestrial ecosystems,
Interngovernmental Panel on Climate Change, 2019a. a
IPCC: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate,
Interngovernmental Panel on Climate Change, 2019b. a
Jeffery, M. L., Gütschow, J., and Gieseke, R.: PRIMAP-CRF: UNFCCC
CRF Data in IPCC 2006 Categories, GFZ Data Services, https://doi.org/10.5880/pik.2018.001, 2018a. a
Jeffery, M. L., Gütschow, J., Gieseke, R., and Gebel, R.: PRIMAP-crf: UNFCCC CRF data in IPCC 2006 categories, Earth Syst. Sci. Data, 10, 1427–1438, https://doi.org/10.5194/essd-10-1427-2018, 2018b. a
Köhl, M., Neupane, P. R., and Lotfiomran, N.: The impact of tree age on
biomass growth and carbon accumulation capacity: A retrospective analysis
using tree ring data of three tropical tree species grown in natural forests
of Suriname, PLOS ONE, 12, e0181187,
https://doi.org/10.1371/journal.pone.0181187, 2017. a
Leimbach, M., Kriegler, E., Roming, N., and Schwanitz, J.: Future growth
patterns of world regions – A GDP scenario approach, Global Environ.
Change, 42, 215–225,
https://doi.org/10.1016/j.gloenvcha.2015.02.005, 2017. a
Meinshausen, M., Hare, B., Wigley, T. M. M., Vuuren, D. V., Elzen, M. G. J. D., and Swart, R.: Multi-gas Emissions Pathways to Meet Climate Targets, Clim. Change, 75, 151–194, https://doi.org/10.1007/s10584-005-9013-2, 2006. a
Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration, Atmos. Chem. Phys., 11, 1417–1456, https://doi.org/10.5194/acp-11-1417-2011, 2011. a
Myllyvirta, L. and Dahiya, S.: Analysis: India's CO2 emissions fall for
first time in four decades amid coronavirus, available at:
https://www.carbonbrief.org/analysis-indias-co2-emissions-fall-for-first-time-in-four-decades-amid -coronavirus?utm_campaign=RevueCBWeeklyBriefing
&utm _medium=email&utm_source=Revue newsletter, last access: 12 May 2020. a
Nabel, J. E., Rogelj, J., Chen, C. M., Markmann, K., Gutzmann, D. J., and
Meinshausen, M.: Decision support for international climate policy
– The PRIMAP emission module, Environ. Model.
Softw., 26, 1419–1433, https://doi.org/10.1016/j.envsoft.2011.08.004, 2011. a
Pauw, W. P., Cassanmagnano, D., Mbeva, K., Hein, J., Guarin, A., Brandi, C.,
Dzebo, A., Canales, N., Adams, K. M., Atteridge, A., Bock, T., Helms, J.,
Zalewski, A., Frommé, E., Lindener, A., and Muhammad, D.: NDC Explorer,
https://doi.org/10.23661/ndc_explorer_2017_2.0, 2016. a
PIK: Paris Reality Check: PRIMAP-hist, available at:
https://www.pik-potsdam.de/paris-reality-check/primap-hist/,
last access: 5 October 2020. a
Pompeo, M. R.: On the U.S. Withdrawal from the Paris Agreement, available at:
https://www.state.gov/on-the-u-s-withdrawal-from-the-paris-agreement/
(last access: 16 June 2020), 2019. a
Pugh, T. A. M., Lindeskog, M., Smith, B., Poulter, B., Arneth, A., Haverd, V., and Calle, L.: Role of forest regrowth in global carbon sink dynamics,
P. Natl. Acad. Sci. USA, 116, 4382–4387,
https://doi.org/10.1073/pnas.1810512116, 2019. a
Rahmstorf, S.: Anthropogenic Climate Change: Revisiting the Facts, Brookings
Institution Press, Washington, 2008. a
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp,
A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S.,
Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Silva, L.
A. D., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T.,
Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M.,
Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z.,
Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.:
The Shared Socioeconomic Pathways and their energy, land use, and greenhouse
gas emissions implications: An overview, Global Environ. Change, 42,
153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017. a, b
Rocha, M. R., Krapp, M., Gütschow, J., Jeffery, M. L., Hare, B., and
Schaeffer, M.: Historical Responsibility for Climate Change – from countries
emissions to contribution to temperature increase, Climate Analytics,
https://doi.org/10.13140/RG.2.2.31296.84481, 2015. a
Rogelj, J., den Elzen, M., Höhne, N., Fransen, T., Fekete, H., Winkler, H.,
Schaeffer, R., Sha, F., Riahi, K., and Meinshausen, M.: Paris Agreement
climate proposals need a boost to keep warming well below 2 ∘C,
Nature, 534, 631–639, https://doi.org/10.1038/nature18307, 2016. a
Rogelj, J., Fricko, O., Meinshausen, M., Krey, V., Zilliacus, J. J. J., and
Riahi, K.: Understanding the origin of Paris Agreement emission
uncertainties, Nature Commun., 8, 15748, https://doi.org/10.1038/ncomms15748, 2017. a, b, c
Roman-Cuesta, R. M., Herold, M., Rufino, M. C., Rosenstock, T. S., Houghton, R. A., Rossi, S., Butterbach-Bahl, K., Ogle, S., Poulter, B., Verchot, L., Martius, C., and de Bruin, S.: Multi-gas and multi-source comparisons of six land use emission datasets and AFOLU estimates in the Fifth Assessment Report, for the tropics for 2000–2005, Biogeosciences, 13, 5799–5819, https://doi.org/10.5194/bg-13-5799-2016, 2016. a
Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E. A.,
Haberl, H., Harper, R., House, J., Jafari, M., Masera, O., Mbow, C.,
Ravindranath, N. H., Rice, C. W., Abad, C. R., Romanovskaya, A., Sperling,
F., and Tubiello, F.: Agriculture, Forestry and Other Land Use (AFOLU),
Cambridge University Press, Cambridge, UK, New York, NY, USA,
2014a. a
Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., Elsiddig, E. A.,
Haberl, H., Harper, R., House, J., Jafari, M., Masera, O., Mbow, C.,
Ravindranath, N. H., Rice, C. W., Abad, C. R., Romanovskaya, A., Sperling,
F., and Tubiello, F.: Agriculture, Forestry and Other Land Use (AFOLU), in:
Climate Change 2014: Mitigation of Climate Change. Contribution of Working
Group III to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., and Minx, J. C., Cambridge University Press, Cambridge, UK,
New York, NY, USA, available at:
https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter11.pdf (last access: 13 February 2020), 2014b. a
Stephenson, N. L., Das, A. J., Condit, R., Russo, S. E., Baker, P. J., Beckman, N. G., Coomes, D. A., Lines, E. R., Morris, W. K., Rüger, N.,
Álvarez, E., Blundo, C., Bunyavejchewin, S., Chuyong, G., Davies,
S. J., Duque, Á., Ewango, C. N., Flores, O., Franklin, J. F., Grau,
H. R., Hao, Z., Harmon, M. E., Hubbell, S. P., Kenfack, D., Lin, Y., Makana,
J.-R., Malizia, A., Malizia, L. R., Pabst, R. J., Pongpattananurak, N., Su,
S.-H., Sun, I.-F., Tan, S., Thomas, D., van Mantgem, P. J., Wang, X., Wiser,
S. K., and Zavala, M. A.: Rate of tree carbon accumulation increases
continuously with tree size, Nature, 507, 90–93, https://doi.org/10.1038/nature12914,
2014. a
Taibi, F.-Z. and Konrad, S.: Pocket Guide to NDCs under the UNFCCC,
available at: https://pubs.iied.org/pdfs/G04320.pdf (last access: 22 September 2019), 2018. a
The World Bank: Nationally Determined Contributions (NDCs),
available at: http://spappssecext.worldbank.org/sites/indc/Pages/mitigation.aspx (last access: 5 April 2020), 2016. a
Tubiello, F. N., Salvatore, M., Ferrara, A. F., House, J., Federici, S., Rossi, S., Biancalani, R., Golec, R. D. C., Jacobs, H., Flammini, A., Prosperi, P., Cardenas-Galindo, P., Schmidhuber, J., Sanchez, M. J. S., Srivastava, N., and Smith, P.: The Contribution of Agriculture, Forestry and other Land Use activities to Global Warming, 1990–2012, Glob. Change Biol., 21, 2655–2660, https://doi.org/10.1111/gcb.12865, 2015. a
UNFCCC: INDC Submission Pages, available at:
https://www4.unfccc.int/sites/submissions/INDC/Submission Pages/submissions.aspx, last access: 1 May 2020a. a
UNFCCC: NDC Registry (interim), available at:
https://www4.unfccc.int/sites/NDCStaging/Pages/All.aspx, last access: 1 May 2020b. a
UNFCCC: Glossary of climate change acronyms and terms, available at:
https://unfccc.int/process-and-meetings/the-convention/glossary-of-climate-change-acronyms-and-terms#e, last access: 16 October 2020c. a
UNFCCC: Doha amendment to the Kyoto Protocol, Decision 1/CMP.8, available at:
https://unfccc.int/files/kyoto_protocol/application/pdf/kp_doha_amendment_english.pdf (last access: 14 May 2020),
2012. a
UNFCCC: National Inventory Submissions 2018, available at:
https://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2018 (last access: 23 July 2019), 2018. a
UNFCCC: UNFCCC Greenhouse Gas Inventory Data – Detailed Data by
Party, available at: http://di.unfccc.int/detailed_data_by_party, last access: 23 July 2019a. a
UNFCCC: Submitted Biennial Update Reports (BURs) from Non-Annex I
Parties, available at: https://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-convention/biennial-update-reports-0 (last access: 23 Jnuary 2020), 2019b. a
UNFCCC: National Inventory Submissions 2019, available at:
https://unfccc.int/process-and-meetings/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2019, last access: 25 October 2019c. a
UNFCCC: Report of the Conference of the Parties serving as the meeting of the
Parties to the Paris Agreement on the third part of its first session, held
in Katowice from 2 to 15 December 2018. Addendum. Part two: Action taken by
the Conference of the Parties serving as the meeting of the Parties to the
Paris Agreement. Decisions adopted by the Conference of the Parties serving
as the meeting of the Parties to the Paris Agreement,
FCCC/PA/CMA/2018/3/Add.2, 2019d. a, b, c
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard,
K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T.,
Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Clim. Change, 109,
5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011.
a
Short summary
The mitigation components of the nationally determined contributions (NDCs) under the Paris Agreement are essential in our fight against climate change. Regular updates with increased ambition are requested to limit global warming to 1.5–2 °C. The new and easy-to-update open-source tool NDCmitiQ can be used to quantify the NDCs' mitigation targets and construct resulting emissions pathways. In use cases, we show target uncertainties from missing clarity, data, and methodological challenges.
The mitigation components of the nationally determined contributions (NDCs) under the Paris...
