Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7177-2022
© Author(s) 2022. 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-15-7177-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Sep 2022
Development and technical paper |
| 23 Sep 2022
| 23 Sep 2022
Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0
Chahan M. Kropf
CORRESPONDING AUTHOR
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Alessio Ciullo
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Laura Otth
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Simona Meiler
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Arun Rana
Frankfurt School of Finance and Management Gemeinnützige GmbH, Adickesallee 32–34, 60322 Frankfurt am Main, Germany
Emanuel Schmid
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Jamie W. McCaughey
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
David N. Bresch
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Related authors
Gregor Ortner, Michael Bründl, Chahan M. Kropf, Thomas Röösli, Yves Bühler, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 23, 2089–2110, https://doi.org/10.5194/nhess-23-2089-2023, https://doi.org/10.5194/nhess-23-2089-2023, 2023
Short summary
Short summary
This paper presents a new approach to assess avalanche risk on a large scale in mountainous regions. It combines a large-scale avalanche modeling method with a state-of-the-art probabilistic risk tool. Over 40 000 individual avalanches were simulated, and a building dataset with over 13 000 single buildings was investigated. With this new method, risk hotspots can be identified and surveyed. This enables current and future risk analysis to assist decision makers in risk reduction and adaptation.
Luca G. Severino, Chahan M. Kropf, Hilla Afargan-Gerstman, Christopher Fairless, Andries Jan de Vries, Daniela I. V. Domeisen, and David N. Bresch
EGUsphere, https://doi.org/10.5194/egusphere-2023-205, https://doi.org/10.5194/egusphere-2023-205, 2023
Short summary
Short summary
This study uses climate projections from 30 climate models and a climate-risk assessment model to obtain projections of future windstorm damage over Europe. We find the damage projections to be highly uncertain and dominated by climate model uncertainty. We find potential increases in windstorm damage over western, and northern-central Europe, and a decrease over the rest of Europe. Our findings demonstrate the importance of climate model uncertainty for the projections of windstorms in Europe.
Timo Schmid, Raphael Portmann, Leonie Villiger, Katharina Schröer, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 24, 847–872, https://doi.org/10.5194/nhess-24-847-2024, https://doi.org/10.5194/nhess-24-847-2024, 2024
Short summary
Short summary
Hailstorms cause severe damage to buildings and cars, which motivates a detailed risk assessment. Here, we present a new open-source hail damage model based on radar data in Switzerland. The model successfully estimates the correct order of magnitude of car and building damages for most large hail events over 20 years. However, large uncertainty remains in the geographical distribution of modelled damages, which can be improved for individual events by using crowdsourced hail reports.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
EGUsphere, https://doi.org/10.5194/egusphere-2024-93, https://doi.org/10.5194/egusphere-2024-93, 2024
Short summary
Short summary
River floods are among the most devastating natural disasters. We propose a flood model with statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite detected floods, we show that it can be used for forecasting the magnitude of flood events in terms of humanitarian impacts and for comparing these with past events.
Raphael Portmann, Timo Schmid, Leonie Villiger, David N. Bresch, and Pierluigi Calanca
EGUsphere, https://doi.org/10.5194/egusphere-2023-2598, https://doi.org/10.5194/egusphere-2023-2598, 2023
Short summary
Short summary
The study presents a model to determine the occurrence of hail damage to field crops (wheat, maize, barley, rapeseed) and grapevines after a hailstorm. Using radar, agricultural land use data, and damage reports, it finds that the model performs best in the main production areas and at 8 km resolution for field crops and 1 km for grapevines. It also highlights the trade-offs in selecting suitable hail size thresholds for modeling, which eventually depends on user needs and cost considerations.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Veronique Bouchet, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Detlef Stammer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-376, https://doi.org/10.5194/essd-2023-376, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines are proposed as international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Gregor Ortner, Michael Bründl, Chahan M. Kropf, Thomas Röösli, Yves Bühler, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 23, 2089–2110, https://doi.org/10.5194/nhess-23-2089-2023, https://doi.org/10.5194/nhess-23-2089-2023, 2023
Short summary
Short summary
This paper presents a new approach to assess avalanche risk on a large scale in mountainous regions. It combines a large-scale avalanche modeling method with a state-of-the-art probabilistic risk tool. Over 40 000 individual avalanches were simulated, and a building dataset with over 13 000 single buildings was investigated. With this new method, risk hotspots can be identified and surveyed. This enables current and future risk analysis to assist decision makers in risk reduction and adaptation.
Luca G. Severino, Chahan M. Kropf, Hilla Afargan-Gerstman, Christopher Fairless, Andries Jan de Vries, Daniela I. V. Domeisen, and David N. Bresch
EGUsphere, https://doi.org/10.5194/egusphere-2023-205, https://doi.org/10.5194/egusphere-2023-205, 2023
Short summary
Short summary
This study uses climate projections from 30 climate models and a climate-risk assessment model to obtain projections of future windstorm damage over Europe. We find the damage projections to be highly uncertain and dominated by climate model uncertainty. We find potential increases in windstorm damage over western, and northern-central Europe, and a decrease over the rest of Europe. Our findings demonstrate the importance of climate model uncertainty for the projections of windstorms in Europe.
Zélie Stalhandske, Valentina Nesa, Marius Zumwald, Martina S. Ragettli, Alina Galimshina, Niels Holthausen, Martin Röösli, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 22, 2531–2541, https://doi.org/10.5194/nhess-22-2531-2022, https://doi.org/10.5194/nhess-22-2531-2022, 2022
Short summary
Short summary
We model the impacts of heat on both mortality and labour productivity in Switzerland in a changing climate. We estimate 658 heat-related death currently per year in Switzerland and CHF 665 million in losses in labour productivity. Should we remain on a high-emissions pathway, these values may double or even triple by the end of the century. Under a lower-emissions scenario impacts are expected to slightly increase and peak by around mid-century.
Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
Short summary
Short summary
In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Samuel Eberenz, Samuel Lüthi, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 21, 393–415, https://doi.org/10.5194/nhess-21-393-2021, https://doi.org/10.5194/nhess-21-393-2021, 2021
Short summary
Short summary
Asset damage caused by tropical cyclones is often computed based on impact functions mapping wind speed to damage. However, a lack of regional impact functions can lead to a substantial bias in tropical cyclone risk estimates. Here, we present regionally calibrated impact functions, as well as global risk estimates. Our results are relevant for researchers, model developers, and practitioners in the context of global risk assessments, climate change adaptation, and physical risk disclosure.
Christoph Welker, Thomas Röösli, and David N. Bresch
Nat. Hazards Earth Syst. Sci., 21, 279–299, https://doi.org/10.5194/nhess-21-279-2021, https://doi.org/10.5194/nhess-21-279-2021, 2021
Short summary
Short summary
How representative are local building insurers' claims to assess winter windstorm risk? In our case study of Zurich, we use a risk model for windstorm building damages and compare three different inputs: insurance claims and historical and probabilistic windstorm datasets. We find that long-term risk is more robustly assessed based on windstorm datasets than on claims data only. Our open-access method allows European building insurers to complement their risk assessment with modelling results.
David N. Bresch and Gabriela Aznar-Siguan
Geosci. Model Dev., 14, 351–363, https://doi.org/10.5194/gmd-14-351-2021, https://doi.org/10.5194/gmd-14-351-2021, 2021
Short summary
Short summary
Climate change is a fact and adaptation a necessity. The Economics of Climate Adaptation methodology provides a framework to integrate risk and reward perspectives of different stakeholders, underpinned by the CLIMADA impact modelling platform. This extended version of CLIMADA enables risk assessment and options appraisal in a modular form and occasionally bespoke fashion yet with high reusability of functionalities to foster usage in interdisciplinary studies and international collaboration.
Samuel Eberenz, Dario Stocker, Thomas Röösli, and David N. Bresch
Earth Syst. Sci. Data, 12, 817–833, https://doi.org/10.5194/essd-12-817-2020, https://doi.org/10.5194/essd-12-817-2020, 2020
Short summary
Short summary
The modeling of economic disaster risk on a global scale requires high-resolution maps of exposed asset values. We have developed a generic and scalable method to downscale national asset value estimates proportional to a combination of nightlight intensity and population data. Here, we present the methodology together with an evaluation of its performance for the subnational downscaling of GDP. The resulting exposure data for 224 countries and the open-source Python code are available online.
Gabriela Aznar-Siguan and David N. Bresch
Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-12-3085-2019, https://doi.org/10.5194/gmd-12-3085-2019, 2019
Short summary
Short summary
The need for assessing the risk of weather events is ever increasing. In addition to quantification of risk today, the role of aggravating factors such as population growth and changing climate conditions matter too. We present the open-source software CLIMADA, which integrates hazard, exposure, and vulnerability to compute metrics to assess risk and to quantify socio-economic impact, and use it to estimate and contextualize the damage of hurricane Irma through the Caribbean in 2017.
Elisabeth Maidl, David N. Bresch, and Matthias Buchecker
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2018-393, https://doi.org/10.5194/nhess-2018-393, 2019
Publication in NHESS not foreseen
Short summary
Short summary
Natural hazard risk management today aims to involve all actors possibly affected by damage. Citizens are regarded as responsible actors in risk mitigation. Practitioners therefore face the challenge of building social capacity towards such a culture of risk. Research on capacity building in Alpine countries, however, so far lacks empirical evidence on individual preparedness in the common population. This study for the first time provides insights for research and practice.
Tobias Geiger, Katja Frieler, and David N. Bresch
Earth Syst. Sci. Data, 10, 185–194, https://doi.org/10.5194/essd-10-185-2018, https://doi.org/10.5194/essd-10-185-2018, 2018
Short summary
Short summary
Tropical cyclones (TCs) pose a major risk to societies worldwide but very limited data exist on their socioeconomic impacts. Here, we apply a common wind field model to comprehensively and consistently estimate the number of people and the sum of assets exposed by all TCs between 1950 and 2015. This information is crucial to assess changes in societal vulnerabilites, to calibrate TC damage functions, and to make risk data more accessible to non-experts and stakeholders.
Related subject area
Climate and Earth system modeling
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
The Regional Climate-Chemistry-Ecology Coupling Model RegCM-Chem (v4.6)-YIBs (v1.0): Development and Application
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
An emulation-based approach for interrogating reactive transport models
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
Short summary
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
Short summary
Short summary
We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
Short summary
Short summary
Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
Short summary
Short summary
We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
Short summary
Short summary
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
Short summary
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
Short summary
Short summary
This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
Short summary
Short summary
The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary
Short summary
Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
Short summary
Short summary
We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
Short summary
Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
Short summary
Short summary
We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Cited articles
Anderson, W., Guikema, S., Zaitchik, B., and Pan, W.: Methods for Estimating
Population Density in Data-Limited Areas: Evaluating Regression and
Tree-Based Models in Peru, PLOS ONE, 9, e100037,
https://doi.org/10.1371/journal.pone.0100037, 2014. a
Berger, L.: Leaving No One Off The Map:
A Guide For Gridded Population Data For
Sustainable Development,
A Report by the Thematic Research Network on Data and Statistics
(TReNDS) of the UN Sustainable Development Solutions Network
(SDSN) in Support of the POPGRID Data Collaborative, https://www.unsdsn.org/leaving-no-one-off-the-map-a-guide-for-gridded-population-data-for-sustainable-development (last access: 28 August 2022), 2020. a
Beven, K. J., Almeida, S., Aspinall, W. P., Bates, P. D., Blazkova, S., Borgomeo, E., Freer, J., Goda, K., Hall, J. W., Phillips, J. C., Simpson, M., Smith, P. J., Stephenson, D. B., Wagener, T., Watson, M., and Wilkins, K. L.: Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas, Nat. Hazards Earth Syst. Sci., 18, 2741–2768, https://doi.org/10.5194/nhess-18-2741-2018, 2018. a, b
Beven, K. J., Aspinall, W. P., Bates, P. D., Borgomeo, E., Goda, K., Hall, J. W., Page, T., Phillips, J. C., Simpson, M., Smith, P. J., Wagener, T., and Watson, M.: Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice?, Nat. Hazards Earth Syst. Sci., 18, 2769–2783, https://doi.org/10.5194/nhess-18-2769-2018, 2018b. a, b
Bloemendaal, N., Haigh, I. D.,de Moel, H., Muis, S.,
Haarsma, R. J., and Aerts, J. C. J. H.: Generation of a Global Synthetic
Tropical Cyclone Hazard Dataset Using STORM, Sci. Data, 7, 40,
https://doi.org/10.1038/s41597-020-0381-2, 2020. a
Borgonovo, E.: A New Uncertainty Importance Measure, Reliab. Eng.
Syst. Safe., 92, 771–784, https://doi.org/10.1016/j.ress.2006.04.015, 2007. a
Borgonovo, E., Lu, X., Plischke, E., Rakovec, O., and Hill, M. C.: Making the
Most out of a Hydrological Model Data Set: Sensitivity Analyses to Open
the Model Black-Box, Water Resour. Res., 53, 7933–7950,
https://doi.org/10.1002/2017WR020767, 2017. a
Bradley, R. and Drechsler, M.: Types of Uncertainty, Erkenn, 79,
1225–1248, https://doi.org/10.1007/s10670-013-9518-4, 2014. a, b
Bradley, R. and Steele, K.: Making Climate Decisions, Philosophy Compass,
10, 799–810, https://doi.org/10.1111/phc3.12259, 2015. a, b
Center for International Earth Science Information Network
(CIESIN): Documentation for the Gridded Population of the
World, Version 4 (GPWv4), Revision 10 Data Sets [data set], https://doi.org/10.7927/H4D50JX4, 2017. a
Ceola, S., Laio, F., and Montanari, A.: Satellite Nighttime Lights Reveal
Increasing Human Exposure to Floods Worldwide, Geophys. Res. Lett.,
41, 7184–7190, https://doi.org/10.1002/2014GL061859, 2014. a
Ciullo, A., Kwakkel, J. H., Bruijn, K. M. D., Doorn, N., and Klijn, F.:
Efficient or Fair? Operationalizing Ethical Principles in Flood
Risk Management: A Case Study on the Dutch-German Rhine, Risk
Anal., 40, 1844–1862, https://doi.org/10.1111/risa.13527, 2020. a
Ciullo, A., Martius, O., Strobl, E., and Bresch, D. N.: A Framework for
Building Climate Storylines Based on Downward Counterfactuals: The Case
of the European Union Solidarity Fund, Climate Risk Management, 33,
100349, https://doi.org/10.1016/j.crm.2021.100349, 2021. a
Cukier, R. I., Fortuin, C. M., Shuler, K. E., Petschek, A. G., and Schaibly,
J. H.: Study of the Sensitivity of Coupled Reaction Systems to Uncertainties
in Rate Coefficients. I Theory, J. Chem. Phys., 59, 3873–3878,
https://doi.org/10.1063/1.1680571, 1973. a
de Moel, H., Asselman, N. E. M., and Aerts, J. C. J. H.: Uncertainty and sensitivity analysis of coastal flood damage estimates in the west of the Netherlands, Nat. Hazards Earth Syst. Sci., 12, 1045–1058, https://doi.org/10.5194/nhess-12-1045-2012, 2012. a
Doorn, N.: The Blind Spot in Risk Ethics: Managing Natural Hazards, Risk Anal.,
35, 354–360, https://doi.org/10.1111/risa.12293, 2015. a
Dottori, F., Di Baldassarre, G., and Todini, E.: Detailed Data Is Welcome, but
with a Pinch of Salt: Accuracy, Precision, and Uncertainty in Flood
Inundation Modeling, Water Resour. Res., 49, 6079–6085,
https://doi.org/10.1002/wrcr.20406, 2013. a, b
Douglas-Smith, D., Iwanaga, T., Croke, B. F. W., and Jakeman, A. J.: Certain
Trends in Uncertainty and Sensitivity Analysis: An Overview of Software
Tools and Techniques, Environ. Modell. Softw., 124, 104588,
https://doi.org/10.1016/j.envsoft.2019.104588, 2020. a, b
Ehre, M., Papaioannou, I., and Straub, D.: A Framework for Global Reliability
Sensitivity Analysis in the Presence of Multi-Uncertainty, Reliab.
Eng. Syst. Safe., 195, 106726,
https://doi.org/10.1016/j.ress.2019.106726, 2020. a
Emanuel, K.: A Fast Intensity Simulator for Tropical Cyclone Risk Analysis, Nat.
Hazards, 88, 779–796, https://doi.org/10.1007/s11069-017-2890-7, 2017. a
Étoré, P., Prieur, C., Pham, D. K., and Li, L.: Global Sensitivity
Analysis for Models Described by Stochastic Differential Equations,
Methodol. Comput. Appl. Probab., 22, 803–831, https://doi.org/10.1007/s11009-019-09732-6,
2020. a
Funtowicz, S. O. and Ravetz, J. R.: Uncertainty and Quality in Science
for Policy, Springer Science & Business Media, https://doi.org/10.1007/978-94-009-0621-1, 1990. a
Gettelman, A., Bresch, D. N., Chen, C. C., Truesdale, J. E., and Bacmeister,
J. T.: Projections of Future Tropical Cyclone Damage with a High-Resolution
Global Climate Model, Climatic Change, 146, 575–585,
https://doi.org/10.1007/s10584-017-1902-7, 2017. a
Ghanem, R., Higdon, D., and Owhadi, H.: Handbook of Uncertainty
Quantification, Springer, New York, NY, 1st Edn., https://doi.org/10.1007/978-3-319-11259-6, 2017. a
Hall, J. W., Tarantola, S., Bates, P. D., and Horritt, M. S.: Distributed
Sensitivity Analysis of Flood Inundation Model Calibration, J.
Hydraul. Eng., 131, 117–126, https://doi.org/10.1061/(ASCE)0733-9429(2005)131:2(117),
2005. a
Hammersley, J. M.: Monte Carlo Methods for Solving Multivariable
Problems, Ann. NY Acad. Sci., 86, 844–874,
https://doi.org/10.1111/j.1749-6632.1960.tb42846.x, 1960. a
Hillger, D., Seaman, C., Liang, C., Miller, S., Lindsey, D., and Kopp, T.:
Suomi NPP VIIRS Imagery Evaluation, J. Geophys. Res.-Atmos., 119, 6440–6455, https://doi.org/10.1002/2013JD021170, 2014. a
Hinkel, J. and Bisaro, A.: Methodological Choices in Solution-Oriented
Adaptation Research: A Diagnostic Framework, Reg. Environ. Change, 16, 7–20,
https://doi.org/10.1007/s10113-014-0682-0, 2016. a, b
Holland, G.: A Revised Hurricane Pressure–Wind Model, Mon.
Wea. Rev., 136, 3432–3445, https://doi.org/10.1175/2008MWR2395.1, 2008. a
Hyde, K. M.: Uncertainty Analysis Methods For Multi-Criteria Decision
Analysis, PhD thesis, The University of Adelaide School of Civil and
Environmental Engineering, Adelaide, 2006. a
IFRC: World Disasters Rreport: Come Heat or High Water., INTL FED OF RED
CROSS, GENEVA, ISBN 978-2-9701289-5-3,
https://media.ifrc.org/ifrc/world-disaster-report-2020 (last access: 28 August 2022), 2020. a
Iooss, B. and Lemaître, P.: A Review on Global Sensitivity Analysis
Methods, in: Uncertainty Management in Simulation-Optimization of
Complex Systems: Algorithms and Applications, edited by: Dellino,
G. and Meloni, C., Operations Research/Computer Science Interfaces
Series, Springer US, Boston, MA, 101–122,
https://doi.org/10.1007/978-1-4899-7547-8_5, 2015. a
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Pachauri, R. K., and Meyer, L. A., IPCC, Geneva, Switzerland, 151 pp., https://www.ipcc.ch/report/ar5/syr/ (last access: 28 August 2022), 2014a. a, b, c
IPCC: 2014: Summary for policymakers, in: Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D.,
Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S.,
Mastrandrea, P. R., and White, L. L., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 1–32, https://www.ipcc.ch/report/ar5/wg2/ (last access: 28 August 2022), 2014b. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp., https://www.ipcc.ch/report/ar6/wg1/ (last access: 28 August 2022), 2021. a
Kam, P. M., Aznar-Siguan, G., Schewe, J., Milano, L., Ginnetti, J., Willner,
S., McCaughey, J. W., and Bresch, D. N.: Global Warming and Population Change
Both Heighten Future Risk of Human Displacement Due to River Floods, Environ.
Res. Lett., 16, 044026, https://doi.org/10.1088/1748-9326/abd26c, 2021. a
Kasprzyk, J. R., Nataraj, S., Reed, P. M., and Lempert, R. J.: Many Objective
Robust Decision Making for Complex Environmental Systems Undergoing Change,
Environ. Modell. Softw., 42, 55–71,
https://doi.org/10.1016/j.envsoft.2012.12.007, 2013. a
Katzav, J., Thompson, E. L., Risbey, J., Stainforth, D. A., Bradley, S., and
Frisch, M.: On the appropriate and inappropriate uses of probability
distributions in climate projections and some alternatives, Climatic Change,
169, 15, https://doi.org/10.1007/s10584-021-03267-x, 2021. a
Kleppek, S., Muccione, V., Raible, C. C., Bresch, D. N., Köllner-Heck,
P., and Stocker, T. F.: Tropical Cyclones in ERA-40: A Detection and
Tracking Method, Geophys. Res. Lett., 35, L10705,
https://doi.org/10.1029/2008GL033880, 2008. a
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.:
The International Best Track Archive for Climate Stewardship
(IBTrACS): Unifying Tropical Cyclone Data, B. Am.
Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010. a
Knüsel, B.: Epistemological Issues in Data-Driven Modeling in
Climate Research, Doctoral Thesis, ETH Zurich,
https://doi.org/10.3929/ethz-b-000399735, 2020. a, b
Knutson, T. R., Sirutis, J. J., Zhao, M., Tuleya, R. E., Bender, M., Vecchi,
G. A., Villarini, G., and Chavas, D.: Global Projections of Intense
Tropical Cyclone Activity for the Late Twenty-First Century from
Dynamical Downscaling of CMIP5/RCP4.5 Scenarios, J.
Climate, 28, 7203–7224, https://doi.org/10.1175/JCLI-D-15-0129.1, 2015. a, b
Koks, E. E., Bočkarjova, M., de Moel, H., and Aerts, J.
C. J. H.: Integrated Direct and Indirect Flood Risk Modeling:
Development and Sensitivity Analysis, Risk Anal., 35, 882–900,
https://doi.org/10.1111/risa.12300, 2015. a
Krauß, W. and Bremer, S.: The Role of Place-Based Narratives of Change in
Climate Risk Governance, Climate Risk Manage., 28, 100221,
https://doi.org/10.1016/j.crm.2020.100221, 2020. a
Kropf, C. M., Schmid, E., Aznar-Siguan, G., Eberenz, S., Vogt, T., Steinmann,
C. B., Röösli, T., Lüthi, S., Sauer, I. J., Mühlhofer, E.,
Hartman, J., Guillod, B. P., Stalhandske, Z., Ciullo, A., Fairless, C., Kam,
P. M. M., wjan262, Meiler, S., Bungener, R., Bozzini, V., Stocker, D., and
Bresch, D. N.: CLIMADA-project/Climada_python: V3.1.0, Zenodo [code],
https://doi.org/10.5281/zenodo.5947271, 2022a. a, b, c, d
Kropf, C. M., Rana, A., and Zhu, Q.: Probabilistic storm surge hazard event set
for Vietnam on 30 arcsecond resolution (2020 and 2050), ETH Research Collection [code and data set],
https://doi.org/10.3929/ethz-b-000566528, 2022b. a
Lemieux, C.: Monte Carlo and Quasi-Monte Carlo Sampling, Springer
Science & Business Media, https://doi.org/10.1007/978-0-387-78165-5, 2009. a, b, c
Leobacher, G. and Pillichshammer, F.: Introduction to Quasi-Monte Carlo
Integration and Applications, Springer, https://doi.org/10.1007/978-3-319-03425-6, 2014. a, b
Marelli, S. and Sudret, B.: UQLab: A Framework for Uncertainty
Quantification in Matlab, in: Second International Conference on
Vulnerability and Risk Analysis and Management (ICVRAM) and
the Sixth International Symposium on Uncertainty, Modeling, and
Analysis (ISUMA), American Society of Civil
Engineers, Liverpool, 2554–2563, https://doi.org/10.1061/9780784413609.257, 2014. a
Marrel, A., Iooss, B., Da Veiga, S., and Ribatet, M.: Global Sensitivity
Analysis of Stochastic Computer Models with Joint Metamodels, Stat. Comput.,
22, 833–847, https://doi.org/10.1007/s11222-011-9274-8, 2012. a
Matott, L. S., Babendreier, J. E., and Purucker, S. T.: Evaluating Uncertainty
in Integrated Environmental Models: A Review of Concepts and Tools, Water
Resour. Res., 45, W06421, https://doi.org/10.1029/2008WR007301, 2009. a, b
Mayer, L. A., Loa, K., Cwik, B., Tuana, N., Keller, K., Gonnerman, C., Parker,
A. M., and Lempert, R. J.: Understanding Scientists' Computational Modeling
Decisions about Climate Risk Management Strategies Using Values-Informed
Mental Models, Global Environ. Chang., 42, 107–116,
https://doi.org/10.1016/j.gloenvcha.2016.12.007, 2017. a, b
Merwade, V., Olivera, F., Arabi, M., and Edleman, S.: Uncertainty in Flood
Inundation Mapping: Current Issues and Future Directions, J.
Hydrol. Eng., 13, 608–620,
https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(608), 2008. a, b
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational
Experiments, Technometrics, 33, 161–174,
https://doi.org/10.1080/00401706.1991.10484804, 1991. a
Norton, J.: An introduction to sensitivity assessment of simulation models,
Environ. Modell. Softw., 69, 166–174,
https://doi.org/10.1016/j.envsoft.2015.03.020, 2015. a
Paleari, L. and Confalonieri, R.: Sensitivity Analysis of a Sensitivity
Analysis: We Are Likely Overlooking the Impact of Distributional
Assumptions, Ecol. Modell., 340, 57–63,
https://doi.org/10.1016/j.ecolmodel.2016.09.008, 2016. a, b
Pianosi, F. and Wagener, T.: A Simple and Efficient Method for Global
Sensitivity Analysis Based on Cumulative Distribution Functions,
Environ. Modell. Softw., 67, 1–11,
https://doi.org/10.1016/j.envsoft.2015.01.004, 2015. a
Plischke, E., Borgonovo, E., and Smith, C. L.: Global Sensitivity Measures from
given Data, Eur. J. Oper. Res., 226, 536–550,
https://doi.org/10.1016/j.ejor.2012.11.047, 2013. a
Saltelli, A.: Making Best Use of Model Evaluations to Compute Sensitivity
Indices, Comput. Phys. Commun., 145, 280–297,
https://doi.org/10.1016/S0010-4655(02)00280-1, 2002. a
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global Sensitivity Analysis: The Primer, John Wiley & Sons, Ltd,
Chichester, England, Hoboken, NJ, ISBN 9780470059975, Online ISBN 9780470725184, https://doi.org/10.1002/9780470725184, 2008. a, b, c
Saltelli, A. and Annoni, P.: How to Avoid a Perfunctory Sensitivity Analysis,
Environ. Modell. Softw., 25, 1508–1517,
https://doi.org/10.1016/j.envsoft.2010.04.012, 2010. a, b, c
Saltelli, A., Guimaraes Pereira, Â., der Sluijs, J.
P. V., and Funtowicz, S.: What Do I Make of Your Latinorumc
Sensitivity Auditing of Mathematical Modelling, International Journal of Foresight and Innovation Policy, 9, 213,
https://doi.org/10.1504/IJFIP.2013.058610, 2013. a
Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst,
N., Li, S., and Wu, Q.: Why so Many Published Sensitivity Analyses Are False:
A Systematic Review of Sensitivity Analysis Practices, Environ.
Modell. Softw., 114, 29–39, https://doi.org/10.1016/j.envsoft.2019.01.012,
2019. a, b, c, d
Sarrazin, F. J., Pianosi, F., and Wagener, T.: Global Sensitivity Analysis
of Environmental Models: Convergence and Validation, Environ.
Modell. Softw., 79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005,
2016. a, b
Savage, J. T. S., Pianosi, F., Bates, P., Freer, J., and Wagener, T.:
Quantifying the Importance of Spatial Resolution and Other Factors through
Global Sensitivity Analysis of a Flood Inundation Model, Water Resour.
Res., 52, 9146–9163, https://doi.org/10.1002/2015WR018198, 2016. a
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S.,
Dima-West, I. M., Fowler, H. J., James, R., Maraun, D., Martius, O.,
Senior, C. A., Sobel, A. H., Stainforth, D. A., Tett, S. F. B., Trenberth,
K. E., van den Hurk, B. J. J. M., Watkins, N. W., Wilby,
R. L., and Zenghelis, D. A.: Storylines: An Alternative Approach to
Representing Uncertainty in Physical Aspects of Climate Change, Climatic
Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9, 2018. a
Sobol′, I. M. and Kucherenko, S.: Derivative Based Global Sensitivity Measures
and Their Link with Global Sensitivity Indices, Mathe. Comput.
Simulat., 79, 3009–3017, https://doi.org/10.1016/j.matcom.2009.01.023, 2009. a
Souvignet, M., Wieneke, F., Müller, L., and Bresch, D. N.: Economics of Climate Adaptation (ECA): Guidebook for Practitioners, KfW Group, KfW Development Bank, Frankfurt am Main, https://www.kfw-entwicklungsbank.de/PDF/Download-Center/Materialien/2016_No6_Guidebook_Economics-of-Climate-Adaptation_EN.pdf (last access: 28 August 2022), 2016. a
Sudret, B.: Global Sensitivity Analysis Using Polynomial Chaos Expansions,
Reliab. Eng. Syst. Safe., 93, 964–979,
https://doi.org/10.1016/j.ress.2007.04.002, 2008. a
Uusitalo, L., Lehikoinen, A., Helle, I., and Myrberg, K.: An Overview of
Methods to Evaluate Uncertainty of Deterministic Models in Decision Support,
Environ. Modell. Softw., 63, 24–31,
https://doi.org/10.1016/j.envsoft.2014.09.017, 2015. a
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, CreateSpace,
Scotts Valley, CA, ISBN 978-1-4414-1269-0, 2009. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman,
K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey,
C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D.,
Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R.,
Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and
van Mulbregt, P.: SciPy 1.0: Fundamental
Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wagenaar, D. J., de Bruijn, K. M., Bouwer, L. M., and de Moel, H.: Uncertainty in flood damage estimates and its potential effect on investment decisions, Nat. Hazards Earth Syst. Sci., 16, 1–14, https://doi.org/10.5194/nhess-16-1-2016, 2016. a
Wagener, T., Reinecke, R., and Pianosi, F.: On the Evaluation of Climate Change
Impact Models, WIREs Clim. Change, 13, e772, https://doi.org/10.1002/wcc.772, 2022. a
Wilby, R. L. and Dessai, S.: Robust Adaptation to Climate Change, Weather, 65,
180–185, https://doi.org/10.1002/wea.543, 2010. a
Zhu, X. and Sudret, B.: Global Sensitivity Analysis for Stochastic Simulators
Based on Generalized Lambda Surrogate Models, Reliab. Eng.
Syst. Safe., 214, 107815, https://doi.org/10.1016/j.ress.2021.107815, 2021. a
Short summary
Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
Mathematical models are approximations, and modellers need to understand and ideally quantify...
