Articles | Volume 15, issue 8
https://doi.org/10.5194/gmd-15-3387-2022
© 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-15-3387-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
22 Apr 2022
Model evaluation paper |
| 22 Apr 2022
Precipitation over southern Africa: is there consensus among global climate models (GCMs), regional climate models (RCMs) and observational data?
Maria Chara Karypidou
CORRESPONDING AUTHOR
Department of Meteorology and Climatology, School of Geology,
Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki,
Greece
Eleni Katragkou
Department of Meteorology and Climatology, School of Geology,
Faculty of Sciences, Aristotle University of Thessaloniki, Thessaloniki,
Greece
Stefan Pieter Sobolowski
NORCE Norwegian Research Centre, Bjerknes Centre for Climate
Research, Bergen, Norway
Related authors
Maria Chara Karypidou, Stefan Pieter Sobolowski, Lorenzo Sangelantoni, Grigory Nikulin, and Eleni Katragkou
Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, https://doi.org/10.5194/gmd-16-1887-2023, 2023
Short summary
Short summary
Southern Africa is listed among the climate change hotspots; hence, accurate climate change information is vital for the optimal preparedness of local communities. In this work we assess the degree to which regional climate models (RCMs) are influenced by the global climate models (GCMs) from which they receive their lateral boundary forcing. We find that although GCMs exert a strong impact on RCMs, RCMs are still able to display substantial improvement relative to the driving GCMs.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Maria Chara Karypidou, Stefan Pieter Sobolowski, Lorenzo Sangelantoni, Grigory Nikulin, and Eleni Katragkou
Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, https://doi.org/10.5194/gmd-16-1887-2023, 2023
Short summary
Short summary
Southern Africa is listed among the climate change hotspots; hence, accurate climate change information is vital for the optimal preparedness of local communities. In this work we assess the degree to which regional climate models (RCMs) are influenced by the global climate models (GCMs) from which they receive their lateral boundary forcing. We find that although GCMs exert a strong impact on RCMs, RCMs are still able to display substantial improvement relative to the driving GCMs.
Stephen Outten, Camille Li, Martin P. King, Lingling Suo, Peter Y. F. Siew, Hoffman Cheung, Richard Davy, Etienne Dunn-Sigouin, Tore Furevik, Shengping He, Erica Madonna, Stefan Sobolowski, Thomas Spengler, and Tim Woollings
Weather Clim. Dynam., 4, 95–114, https://doi.org/10.5194/wcd-4-95-2023, https://doi.org/10.5194/wcd-4-95-2023, 2023
Short summary
Short summary
Strong disagreement exists in the scientific community over the role of Arctic sea ice in shaping wintertime Eurasian cooling. The observed Eurasian cooling can arise naturally without sea-ice loss but is expected to be a rare event. We propose a framework that incorporates sea-ice retreat and natural variability as contributing factors. A helpful analogy is of a dice roll that may result in cooling, warming, or anything in between, with sea-ice loss acting to load the dice in favour of cooling.
Anne Sophie Daloz, Clemens Schwingshackl, Priscilla Mooney, Susanna Strada, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Michal Belda, Tomas Halenka, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 2403–2419, https://doi.org/10.5194/tc-16-2403-2022, https://doi.org/10.5194/tc-16-2403-2022, 2022
Short summary
Short summary
Snow plays a major role in the regulation of the Earth's surface temperature. Together with climate change, rising temperatures are already altering snow in many ways. In this context, it is crucial to better understand the ability of climate models to represent snow and snow processes. This work focuses on Europe and shows that the melting season in spring still represents a challenge for climate models and that more work is needed to accurately simulate snow–atmosphere interactions.
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
Short summary
Short summary
We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
Short summary
Short summary
Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Martin P. King, Camille Li, and Stefan Sobolowski
Weather Clim. Dynam., 2, 759–776, https://doi.org/10.5194/wcd-2-759-2021, https://doi.org/10.5194/wcd-2-759-2021, 2021
Short summary
Short summary
We re-examine the uncertainty of ENSO teleconnection to the North Atlantic by considering the November–December and January–February months in the cold season, in addition to the conventional DJF months. This is motivated by previous studies reporting varying teleconnected atmospheric anomalies and the mechanisms concerned. Our results indicate an improved confidence in the patterns of the teleconnection. The finding may also have implications on research in predictability and climate impact.
Trude Eidhammer, Adam Booth, Sven Decker, Lu Li, Michael Barlage, David Gochis, Roy Rasmussen, Kjetil Melvold, Atle Nesje, and Stefan Sobolowski
Hydrol. Earth Syst. Sci., 25, 4275–4297, https://doi.org/10.5194/hess-25-4275-2021, https://doi.org/10.5194/hess-25-4275-2021, 2021
Short summary
Short summary
We coupled a detailed snow–ice model (Crocus) to represent glaciers in the Weather Research and Forecasting (WRF)-Hydro model and tested it on a well-studied glacier. Several observational systems were used to evaluate the system, i.e., satellites, ground-penetrating radar (used over the glacier for snow depth) and stake observations for glacier mass balance and discharge measurements in rivers from the glacier. Results showed improvements in the streamflow projections when including the model.
Dimitris Akritidis, Eleni Katragkou, Aristeidis K. Georgoulias, Prodromos Zanis, Stergios Kartsios, Johannes Flemming, Antje Inness, John Douros, and Henk Eskes
Atmos. Chem. Phys., 20, 13557–13578, https://doi.org/10.5194/acp-20-13557-2020, https://doi.org/10.5194/acp-20-13557-2020, 2020
Short summary
Short summary
We assess the Copernicus Atmosphere Monitoring Service (CAMS) global and regional forecasts performance during a complex aerosol transport event over Europe induced by the passage of Storm Ophelia in mid-October 2017. Comparison with satellite observations reveals a satisfactory performance of CAMS global forecast assisted by data assimilation, while comparison with ground-based measurements indicates that the CAMS regional system over-performs compared to the global one in terms of air quality.
Vasileios Pavlidis, Eleni Katragkou, Andreas Prein, Aristeidis K. Georgoulias, Stergios Kartsios, Prodromos Zanis, and Theodoros Karacostas
Geosci. Model Dev., 13, 2511–2532, https://doi.org/10.5194/gmd-13-2511-2020, https://doi.org/10.5194/gmd-13-2511-2020, 2020
Short summary
Short summary
Our study investigates the role of aerosols in the climate of Europe by using a computer model and exploring different aerosol options available in this model as well as different aerosol datasets. Results show that aerosols can have a considerable impact on many aspects of the climate. Aerosols reduce solar radiation and temperature at the surface. Precipitation is not particularly affected in any specific direction. The cloudiness amount change is small. Also, changes in wind pattern are seen.
Peter Yu Feng Siew, Camille Li, Stefan Pieter Sobolowski, and Martin Peter King
Weather Clim. Dynam., 1, 261–275, https://doi.org/10.5194/wcd-1-261-2020, https://doi.org/10.5194/wcd-1-261-2020, 2020
Short summary
Short summary
Arctic sea ice loss has been linked to changes in mid-latitude weather and climate. However, the literature offers differing views on the strength, robustness, and even existence of these linkages. We use a statistical tool (Causal Effect Networks) to show that one proposed pathway linking Barents–Kara ice and mid-latitude circulation is intermittent in observations and likely only active under certain conditions. This result may help explain apparent inconsistencies across previous studies.
Edouard L. Davin, Diana Rechid, Marcus Breil, Rita M. Cardoso, Erika Coppola, Peter Hoffmann, Lisa L. Jach, Eleni Katragkou, Nathalie de Noblet-Ducoudré, Kai Radtke, Mario Raffa, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Tölle, Kirsten Warrach-Sagi, and Volker Wulfmeyer
Earth Syst. Dynam., 11, 183–200, https://doi.org/10.5194/esd-11-183-2020, https://doi.org/10.5194/esd-11-183-2020, 2020
Lu Li, Marie Pontoppidan, Stefan Sobolowski, and Alfonso Senatore
Hydrol. Earth Syst. Sci., 24, 771–791, https://doi.org/10.5194/hess-24-771-2020, https://doi.org/10.5194/hess-24-771-2020, 2020
Short summary
Short summary
We assessed the impact of initial conditions on convection-permitting simulations of a flood event over mountainous terrain. The calibrated convection-permitting model performs better than the simpler conceptual model. Discharge is slightly more sensitive to spin-up time than precipitation due to the influence of soil moisture. A maximum of 0.5 m of snow is converted to runoff irrespective of the initial snow depth, and this snowmelt contributes to discharge mostly during peak flow period.
Lluís Fita, Jan Polcher, Theodore M. Giannaros, Torge Lorenz, Josipa Milovac, Giannis Sofiadis, Eleni Katragkou, and Sophie Bastin
Geosci. Model Dev., 12, 1029–1066, https://doi.org/10.5194/gmd-12-1029-2019, https://doi.org/10.5194/gmd-12-1029-2019, 2019
Short summary
Short summary
Regional climate experiments coordinated throughout CORDEX aim to study and provide high-quality climate data over a given region. The data are used in climate change mitigation and adaptation policy studies and by stakeholders. CORDEX requires a list of variables, most of which are not provided by atmospheric models. Aiming to help the community and to maximize the use of CORDEX exercises, we create a new module for WRF models to directly produce them by adding
genericand
additionalones.
Dimitris Akritidis, Eleni Katragkou, Prodromos Zanis, Ioannis Pytharoulis, Dimitris Melas, Johannes Flemming, Antje Inness, Hannah Clark, Matthieu Plu, and Henk Eskes
Atmos. Chem. Phys., 18, 15515–15534, https://doi.org/10.5194/acp-18-15515-2018, https://doi.org/10.5194/acp-18-15515-2018, 2018
Short summary
Short summary
Analysis and evaluation of the Copernicus Atmosphere Monitoring Service (CAMS) global and regional forecast systems during a deep stratosphere-to-troposphere ozone transport event over Europe in January 2017. Radiosondes, satellite images, ozonesondes and aircraft measurements were used to investigate the folding of the tropopause at several European sites and the induced presence of dry and ozone-rich air in the troposphere.
Johannes Flemming, Angela Benedetti, Antje Inness, Richard J. Engelen, Luke Jones, Vincent Huijnen, Samuel Remy, Mark Parrington, Martin Suttie, Alessio Bozzo, Vincent-Henri Peuch, Dimitris Akritidis, and Eleni Katragkou
Atmos. Chem. Phys., 17, 1945–1983, https://doi.org/10.5194/acp-17-1945-2017, https://doi.org/10.5194/acp-17-1945-2017, 2017
Short summary
Short summary
We combine satellite observations of carbon monoxide, ozone and aerosols with the results from a model using a technique called data assimilation. The generated global data set (CAMS interim reanalysis) covers the period 2003–2015 at a resolution of about 110 km. The CAMS interim reanalysis can be used to study global air pollution and climate forcing of aerosol and stratospheric ozone. It has been produced by the Copernicus Atmosphere Monitoring Service (http://atmosphere. copernicus.eu).
Athanasios Tsikerdekis, Prodromos Zanis, Allison L. Steiner, Fabien Solmon, Vassilis Amiridis, Eleni Marinou, Eleni Katragkou, Theodoros Karacostas, and Gilles Foret
Atmos. Chem. Phys., 17, 769–791, https://doi.org/10.5194/acp-17-769-2017, https://doi.org/10.5194/acp-17-769-2017, 2017
Short summary
Short summary
Dust is the most abundant aerosol in the atmosphere, considerably affecting Earth's climate. We use a new dust size discretization that improves the physical representation of dust in a regional climate model. This study is among the first studies evaluating the vertical profile of simulated dust with a pure dust product. The new dust size discretization increases dust optical depth by 10 % over the desert and Mediterranean. Consequently, the dust SW and LW radiative forcing is enhanced by 10 %.
G. Alexandri, A. K. Georgoulias, P. Zanis, E. Katragkou, A. Tsikerdekis, K. Kourtidis, and C. Meleti
Atmos. Chem. Phys., 15, 13195–13216, https://doi.org/10.5194/acp-15-13195-2015, https://doi.org/10.5194/acp-15-13195-2015, 2015
Short summary
Short summary
It is shown here that RegCM4 regional climate model adequately simulates surface solar radiation (SSR) over Europe but significantly over/underestimates several parameters that determine the transmission of solar radiation in the atmosphere. The agreement between RegCM4 and satellite-based SSR observations is actually a result of the conflicting effect of these parameters. We suggest that there should be a reassessment of the way these parameters are represented within this and other models.
H. Eskes, V. Huijnen, A. Arola, A. Benedictow, A.-M. Blechschmidt, E. Botek, O. Boucher, I. Bouarar, S. Chabrillat, E. Cuevas, R. Engelen, H. Flentje, A. Gaudel, J. Griesfeller, L. Jones, J. Kapsomenakis, E. Katragkou, S. Kinne, B. Langerock, M. Razinger, A. Richter, M. Schultz, M. Schulz, N. Sudarchikova, V. Thouret, M. Vrekoussis, A. Wagner, and C. Zerefos
Geosci. Model Dev., 8, 3523–3543, https://doi.org/10.5194/gmd-8-3523-2015, https://doi.org/10.5194/gmd-8-3523-2015, 2015
Short summary
Short summary
The MACC project is preparing the operational atmosphere service of the European Copernicus Programme, and uses data assimilation to combine atmospheric models with available observations. Our paper provides an overview of the aerosol and trace gas validation activity of MACC. Topics are the validation requirements, the measurement data, the assimilation systems, the upgrade procedure, operational aspects and the scoring methods. A summary is provided of recent results, including special events.
E. Katragkou, P. Zanis, A. Tsikerdekis, J. Kapsomenakis, D. Melas, H. Eskes, J. Flemming, V. Huijnen, A. Inness, M. G. Schultz, O. Stein, and C. S. Zerefos
Geosci. Model Dev., 8, 2299–2314, https://doi.org/10.5194/gmd-8-2299-2015, https://doi.org/10.5194/gmd-8-2299-2015, 2015
Short summary
Short summary
This work is an extended evaluation of near-surface ozone as part of the global reanalysis of atmospheric composition, produced within the European-funded project MACC (Monitoring Atmospheric Composition and Climate). It includes an evaluation over the period 2003-2012 and provides an overall assessment of the modelling system performance with respect to near surface ozone for specific European subregions.
A. Inness, A.-M. Blechschmidt, I. Bouarar, S. Chabrillat, M. Crepulja, R. J. Engelen, H. Eskes, J. Flemming, A. Gaudel, F. Hendrick, V. Huijnen, L. Jones, J. Kapsomenakis, E. Katragkou, A. Keppens, B. Langerock, M. de Mazière, D. Melas, M. Parrington, V. H. Peuch, M. Razinger, A. Richter, M. G. Schultz, M. Suttie, V. Thouret, M. Vrekoussis, A. Wagner, and C. Zerefos
Atmos. Chem. Phys., 15, 5275–5303, https://doi.org/10.5194/acp-15-5275-2015, https://doi.org/10.5194/acp-15-5275-2015, 2015
Short summary
Short summary
The paper presents results from data assimilation studies with the new Composition-IFS model developed in the MACC project. This system was used in MACC to produce daily analyses and 5-day forecasts of atmospheric composition and is now run daily in the EU’s Copernicus Atmosphere Monitoring Service. The paper looks at the quality of the CO, O3 and NO2 analysis fields obtained with this system, comparing them against observations, a control run and an older version of the model.
E. Katragkou, M. García-Díez, R. Vautard, S. Sobolowski, P. Zanis, G. Alexandri, R. M. Cardoso, A. Colette, J. Fernandez, A. Gobiet, K. Goergen, T. Karacostas, S. Knist, S. Mayer, P. M. M. Soares, I. Pytharoulis, I. Tegoulias, A. Tsikerdekis, and D. Jacob
Geosci. Model Dev., 8, 603–618, https://doi.org/10.5194/gmd-8-603-2015, https://doi.org/10.5194/gmd-8-603-2015, 2015
Related subject area
Climate and Earth system modeling
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources
Conservation of heat and mass in P-SKRIPS version 1: the coupled atmosphere–ice–ocean model of the Ross Sea
Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53
Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0
Differentiable programming for Earth system modeling
Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales
Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning
An improved method of the Globally Resolved Energy Balance model by the Bayesian networks
Assessing predicted cirrus ice properties between two deterministic ice formation parameterizations
Various ways of using empirical orthogonal functions for climate model evaluation
C-Coupler3.0: an integrated coupler infrastructure for Earth system modelling
FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean
Pace v0.2: a Python-based performance-portable atmospheric model
Simulated stable water isotopes during the mid-Holocene and pre-industrial using AWI-ESM-2.1-wiso
Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water
The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)/RTTOV (v12.3)
Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data
ModE-Sim – A medium size AGCM ensemble to study climate variability during the modern era (1420 to 2009)
The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake
Evaluating wind profiles in a numerical weather prediction model with Doppler lidar
Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin
Modernizing the open-source community Noah-MP land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
URock 2023a: An open source GIS-based wind model for complex urban settings
The impact of lateral boundary forcing in the CORDEX-Africa ensemble over southern Africa
Effects of complex terrain on the shortwave radiative balance: a sub-grid-scale parameterization for the GFDL Earth System Model version 4.1
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
Short summary
Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
Short summary
Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
Short summary
Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
Short summary
Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
Short summary
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
Short summary
Short summary
A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
Short summary
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
Short summary
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
Short summary
Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
Short summary
Short summary
In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
Short summary
Short summary
We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
Short summary
Short summary
We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
Short summary
Short summary
Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
Short summary
Short summary
This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
Short summary
Short summary
Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
Short summary
Short summary
Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
Short summary
Short summary
This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
Short summary
Short summary
Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
Short summary
Short summary
Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
Short summary
Short summary
This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
Short summary
Short summary
A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
Short summary
Short summary
A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
Short summary
Short summary
C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
Short summary
Short summary
FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
Short summary
Short summary
It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-68, https://doi.org/10.5194/gmd-2023-68, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We develpoed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observation and reconstruction. Based on our analyses, the observed isotope-temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach for determining the onset date of the West African summer monsoon.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
Short summary
Short summary
The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023, https://doi.org/10.5194/gmd-16-2343-2023, 2023
Short summary
Short summary
A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-60, https://doi.org/10.5194/gmd-2023-60, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
Short summary
Short summary
This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
EGUsphere, https://doi.org/10.5194/egusphere-2023-209, https://doi.org/10.5194/egusphere-2023-209, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions and volcanic aerosols for the period 1420 to 2009 as model input while accounting for uncertainties in these. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand mechanism that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023, https://doi.org/10.5194/gmd-16-2095-2023, 2023
Short summary
Short summary
Climate projections of a high-CO2 future are highly uncertain. A new study provides a novel approach to identifying key regions that dynamically explain the model uncertainty. To yield an accurate estimate of the future North Atlantic carbon uptake, we find that a correct simulation of the upper- and interior-ocean volume transport at 25–30° N is key. However, results indicate that models rarely perform well for both indicators and point towards inconsistencies within the model ensemble.
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023, https://doi.org/10.5194/gmd-16-2077-2023, 2023
Short summary
Short summary
We used Doppler lidar to evaluate the wind profiles generated by a weather forecast model. We first compared the Doppler lidar observations with co-located radiosonde profiles, and they agree well. The model performs best over marine and coastal locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi
Geosci. Model Dev., 16, 2055–2076, https://doi.org/10.5194/gmd-16-2055-2023, https://doi.org/10.5194/gmd-16-2055-2023, 2023
Short summary
Short summary
Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
EGUsphere, https://doi.org/10.5194/egusphere-2023-675, https://doi.org/10.5194/egusphere-2023-675, 2023
Short summary
Short summary
Noah-MP is one of the most widely-used community land surface models in the world, which is designed for applications ranging from uncoupled land-surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhances the model modularity, interoperability, and applicability.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2023-354, https://doi.org/10.5194/egusphere-2023-354, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This manuscript shows that the new wind model, URock, fits generally well with observations and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students and practitioners).
Maria Chara Karypidou, Stefan Pieter Sobolowski, Lorenzo Sangelantoni, Grigory Nikulin, and Eleni Katragkou
Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, https://doi.org/10.5194/gmd-16-1887-2023, 2023
Short summary
Short summary
Southern Africa is listed among the climate change hotspots; hence, accurate climate change information is vital for the optimal preparedness of local communities. In this work we assess the degree to which regional climate models (RCMs) are influenced by the global climate models (GCMs) from which they receive their lateral boundary forcing. We find that although GCMs exert a strong impact on RCMs, RCMs are still able to display substantial improvement relative to the driving GCMs.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
Short summary
Short summary
In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Cited articles
Adler, R. F., Gu, G., and Huffman, G. J.: Estimating Climatological Bias Errors for the Global Precipitation 118 Climatology Project (GPCP), J. Appl. Meteorol. Clim., 51, 84–99 https://doi.org/10.1175/JAMC-D-11-052.1, 2012.
African Development Bank Group (AFDBG): Southern Africa Economic Outlook 2019, African Development Bank, 2019.
Ashouri, H., Hsu, K.-L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R.,
Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANN-CDR: Daily
Precipitation Climate Data Record from Multisatellite Observations for
Hydrological and Climate Studies, B. Am. Meteorol. Soc., 96, 69–83,
https://doi.org/10.1175/BAMS-D-13-00068.1, 2015.
Azzarri, C. and Signorelli, S.: Climate and poverty in Africa South of the
Sahara, World Dev., 125, 104691,
https://doi.org/10.1016/j.worlddev.2019.104691, 2020.
Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi,
M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belusic, D., Berthou, S.,
Brisson, E., Cardoso, R. M., Chan, S., Christensen, O. B., Fernandez, J.,
Fita, L., Frisius, T., Gasparac, G., Giorgi, F., Goergen, K., Haugen, J. E.,
Hodnebrog, O., Kartsios, S., Katragkou, E., Kendon, E. J., Keuler, K.,
Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D.,
Mercogliano, P., Milovac, J., Panitz, H.-J., Raffa, M., Remedio, A. R.,
Schar, C., Soares, P. M. M., Srnec, L., Steensen, B. M., Stocchi, P., Tolle,
M. H., Truhetz, H., Vergara-Temprado, J., de Vries, H., Warrach-Sagi, K.,
Wulfmeyer, V., and Zander, M.: The first multi-model ensemble of regional
climate simulations at the kilometer-scale resolution, Part I: Evaluation of
precipitation, Clim. Dynam., 57, 275–302, https://doi.org/10.1007/s00382-021-05708-w, 2021.
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Higgins, R. W., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res.-Atmos., 113, D04110, https://doi.org/10.1029/2007JD009132, 2008.
Chen, M., Xie, P., Janowiak, J. E., and Arkin, P. A.: Global Land Precipitation: A 50-yr Monthly Analysis Based on 136 Gauge Observations, J. Hydrometeorol., 3, 249–266, https://doi.org/10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2, 2002.
Chinowsky, P. S., Schweikert, A. E., Strzepek, N. L., and Strzepek, K.:
Infrastructure and climate change: a study of impacts and adaptations in
Malawi, Mozambique, and Zambia, Climatic Change, 130, 49–62,
https://doi.org/10.1007/s10584-014-1219-8, 2015.
Climate Data Store (CDS): https://cds.climate.copernicus.eu/#!/home, last access: 1 September 2021.
Coppola, E., Raffaele, F., Giorgi, F., Giuliani, G., Xuejie, G., Ciarlo, J.
M., Sines, T. R., Torres-Alavez, J. A., Das, S., di Sante, F., Pichelli, E.,
Glazer, R., Müller, S. K., Abba Omar, S., Ashfaq, M., Bukovsky, M., Im,
E.-S., Jacob, D., Teichmann, C., Remedio, A., Remke, T., Kriegsmann, A.,
Bülow, K., Weber, T., Buntemeyer, L., Sieck, K., and Rechid, D.: Climate
hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble,
Clim. Dynam., 57, 1293–1383, https://doi.org/10.1007/s00382-021-05640-z,
2021.
Crétat, J., Pohl, B., Dieppois, B., Berthou, S., and Pergaud, J.: The
Angola Low: relationship with southern African rainfall and ENSO, Clim.
Dynam., 52, 1783–1803, https://doi.org/10.1007/s00382-018-4222-3, 2019.
Daron, J., Burgin, L., Janes, T., Jones, R. G., and Jack, C.: Climate
process chains: Examples from southern Africa, Int. J. Climatol., 39,
4784–4797, https://doi.org/10.1002/joc.6106, 2019.
Davis-Reddy, C. L. and Vincent, K.: Climate Risk and Vulnerability: A Handbook for Southern Africa, 2nd Edn., CSIR, Pretoria, South Africa, 2017.
Denis, B., Laprise, R., and Caya, D.: Sensitivity of a regional climate
model to the resolution of the lateral boundary conditions, Clim. Dynam., 20,
107–126, https://doi.org/10.1007/s00382-002-0264-6, 2003.
Desbiolles, F., Howard, E., Blamey, R. C., Barimalala, R., Hart, N. C. G.,
and Reason, C. J. C.: Role of ocean mesoscale structures in shaping the
Angola-Low pressure system and the southern Africa rainfall, Clim. Dynam., 54,
3685–3704, https://doi.org/10.1007/s00382-020-05199-1, 2020.
Dieppois, B., Pohl, B., Crétat, J., Eden, J., Sidibe, M., New, M.,
Rouault, M., and Lawler, D.: Southern African summer-rainfall variability,
and its teleconnections, on interannual to interdecadal timescales in CMIP5
models, Clim. Dynam., 53, 3505–3527,
https://doi.org/10.1007/s00382-019-04720-5, 2019.
Diffenbaugh, N. S. and Giorgi, F.: Climate change hotspots in the CMIP5
global climate model ensemble, Climatic Change, 114, 813–822,
https://doi.org/10.1007/s10584-012-0570-x, 2012.
Di Luca, A., de Elía, R., and Laprise, R.: Potential for added value in
temperature simulated by high-resolution nested RCMs in present climate and
in the climate change signal, Clim. Dynam., 40, 443–464,
https://doi.org/10.1007/s00382-012-1384-2, 2013.
Dosio, A. and Panitz, H.-J.: Climate change projections for CORDEX-Africa
with COSMO-CLM regional climate model and differences with the driving
global climate models, Clim. Dynam., 46, 1599–1625,
https://doi.org/10.1007/s00382-015-2664-4, 2016.
Dosio, A., Panitz, H.-J., Schubert-Frisius, M., and Lüthi, D.: Dynamical
downscaling of CMIP5 global circulation models over CORDEX-Africa with
COSMO-CLM: evaluation over the present climate and analysis of the added
value, Clim. Dynam., 44, 2637–2661,
https://doi.org/10.1007/s00382-014-2262-x, 2015.
Dosio, A., Jones, R. G., Jack, C., Lennard, C., Nikulin, G., and Hewitson,
B.: What can we know about future precipitation in Africa? Robustness,
significance and added value of projections from a large ensemble of
regional climate models, Clim. Dynam., 53, 5833–5858,
https://doi.org/10.1007/s00382-019-04900-3, 2019.
Dosio, A., Jury, M. W., Almazroui, M., Ashfaq, M., Diallo, I., Engelbrecht,
F. A., Klutse, N. A. B., Lennard, C., Pinto, I., Sylla, M. B., and Tamoffo,
A. T.: Projected future daily characteristics of African precipitation based
on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX-CORE) climate models,
Clim. Dynam., 57, 3135–3158, https://doi.org/10.1007/s00382-021-05859-w,
2021.
Duan, H., Zhang, G., Wang, S., and Fan, Y.: Robust climate change research:
a review on multi-model analysis, Environ. Res. Lett., 14, 033001,
https://doi.org/10.1088/1748-9326/aaf8f9, 2019.
ESGF-DKRZ: https://esgf-data.dkrz.de/projects/esgf-dkrz/, last access: 1 September 2021.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards infrared precipitation with stations – a new environmental
record for monitoring extremes, Sci. Data, 2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015.
Giesen, N. van de, Hut, R., and Selker, J.: The Trans-African
Hydro-Meteorological Observatory (TAHMO), WIREs Water, 1, 341–348,
https://doi.org/10.1002/wat2.1034, 2014.
Giorgi, F.: Thirty Years of Regional Climate Modeling: Where Are We and
Where Are We Going next?, J. Geophys. Res.-Atmos., 124, 5696–5723,
https://doi.org/10.1029/2018JD030094, 2019.
Giorgi, F. and Gutowski, W. J.: Regional Dynamical Downscaling and the
CORDEX Initiative, Annu. Rev. Environ. Res., 40, 467–490,
https://doi.org/10.1146/annurev-environ-102014-021217, 2015.
Giorgi, F., Coppola, E., Raffaele, F., Diro, G. T., Fuentes-Franco, R.,
Giuliani, G., Mamgain, A., Llopart, M. P., Mariotti, L., and Torma, C.:
Changes in extremes and hydroclimatic regimes in the CREMA ensemble
projections, Climatic Change, 125, 39–51,
https://doi.org/10.1007/s10584-014-1117-0, 2014.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated high-resolution grids of monthly climatic 169 observations – the CRU TS3.10 Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hart, N. C. G., Reason, C. J. C., and Fauchereau, N.:
Tropical–Extratropical Interactions over Southern Africa: Three Cases of
Heavy Summer Season Rainfall, Mon. Weather Rev., 138, 2608–2623,
https://doi.org/10.1175/2010MWR3070.1, 2010.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.
de, Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Houze, R. A.: Orographic effects on precipitating clouds, Rev. Geophys., 50, RG1001,
https://doi.org/10.1029/2011RG000365, 2012.
Howard, E. and Washington, R.: Characterizing the Synoptic Expression of the
Angola Low, J. Climate, 31, 7147–7165,
https://doi.org/10.1175/JCLI-D-18-0017.1, 2018.
Howard, E. and Washington, R.: Drylines in Southern Africa: Rediscovering
the Congo Air Boundary, J. Climate, 32, 8223–8242,
https://doi.org/10.1175/JCLI-D-19-0437.1, 2019.
IPCC: Annex I: Atlas of Global and Regional Climate Projections, in: Climate
Change 2013: The Physical Science Basis, Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate
Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M.,
Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.
M., Cambridge University Press, Cambridge, United Kingdom and New York, NY,
USA, 1311–1394, https://doi.org/10.1017/CBO9781107415324.029, 2013.
Janowiak, J. E.: An Investigation of Interannual Rainfall Variability in
Africa, J. Climate, 1, 240–255,
https://doi.org/10.1175/1520-0442(1988)001<0240:AIOIRV>,2.0.CO;2, 1988.
Kalognomou, E.-A., Lennard, C., Shongwe, M., Pinto, I., Favre, A., Kent, M.,
Hewitson, B., Dosio, A., Nikulin, G., Panitz, H.-J., and Büchner, M.: A
Diagnostic Evaluation of Precipitation in CORDEX Models over Southern
Africa, J. Climate, 26, 9477–9506, https://doi.org/10.1175/JCLI-D-12-00703.1,
2013.
Karypidou, M. C.: Precipitation over southern Africa: Is there consensus among GCMs, RCMs and observational data? (Version 1), Zenodo [data set], https://doi.org/10.5281/zenodo.4725441, 2021.
Kendall, M. G.: Rank correlation methods, Griffin, Oxford, England, 1948.
Kim, J., Waliser, D. E., Mattmann, C. A., Goodale, C. E., Hart, A. F.,
Zimdars, P. A., Crichton, D. J., Jones, C., Nikulin, G., Hewitson, B., Jack,
C., Lennard, C., and Favre, A.: Evaluation of the CORDEX-Africa multi-RCM
hindcast: systematic model errors, Clim. Dynam., 42, 1189–1202,
https://doi.org/10.1007/s00382-013-1751-7, 2014.
Kim, Y.-H., Min, S.-K., Zhang, X., Sillmann, J., and Sandstad, M.:
Evaluation of the CMIP6 multi-model ensemble for climate extreme indices,
Weather Clim. Extrem., 29, 100269,
https://doi.org/10.1016/j.wace.2020.100269, 2020.
Kula, N., Haines, A., and Fryatt, R.: Reducing Vulnerability to Climate
Change in Sub-Saharan Africa: The Need for Better Evidence, PLoS Med., 10, e1001374,
https://doi.org/10.1371/journal.pmed.1001374, 2013.
Laprise, R., de Elía, R., Caya, D., Biner, S., Lucas-Picher, P.,
Diaconescu, E., Leduc, M., Alexandru, A., Separovic, L., and Canadian
Network for Regional Climate Modelling and Diagnostics: Challenging some
tenets of Regional Climate Modelling, Meteorol. Atmos. Phys., 100,
3–22, https://doi.org/10.1007/s00703-008-0292-9, 2008.
Le Coz, C. and van de Giesen, N.: Comparison of Rainfall Products over
Sub-Saharan Africa, J. Hydrometeorol., 21, 553–596,
https://doi.org/10.1175/JHM-D-18-0256.1, 2020.
L'Heureux, M. L., Lee, S., and Lyon, B.: Recent multidecadal strengthening
of the Walker circulation across the tropical Pacific, Nat. Clim. Change, 3,
571–576, https://doi.org/10.1038/nclimate1840, 2013.
Lorenz, C. and Kunstmann, H.: The Hydrological Cycle in Three
State-of-the-Art Reanalyses: Intercomparison and Performance Analysis, J.
Hydrometeorol., 13, 1397–1420, https://doi.org/10.1175/JHM-D-11-088.1,
2012.
Lyon, B. and Mason, S. J.: The 1997–98 Summer Rainfall Season in Southern
Africa. Part I: Observations, J. Climate, 20, 5134–5148,
https://doi.org/10.1175/JCLI4225.1, 2007.
Mabhaudhi, T., Mpandeli, S., Nhamo, L., Chimonyo, V. G. P., Nhemachena, C.,
Senzanje, A., Naidoo, D., and Modi, A. T.: Prospects for Improving Irrigated
Agriculture in Southern Africa: Linking Water, Energy and Food, Water, 10,
1881, https://doi.org/10.3390/w10121881, 2018.
Maidment, R. I., Grimes, D., Allan, R. P., Tarnavsky, E., Stringer, M.,
Hewison, T., Roebeling, R., and Black, E.: The 30 year TAMSAT African
Rainfall Climatology And Time series (TARCAT) data set, J. Geophys. Res.-Atmos., 119, 10619–10644, https://doi.org/10.1002/2014JD021927, 2014.
Maidment, R. I., Allan, R. P., and Black, E.: Recent observed and simulated
changes in precipitation over Africa, Geophys. Res. Lett., 42, 8155–8164,
https://doi.org/10.1002/2015GL065765, 2015.
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259,
https://doi.org/10.2307/1907187, 1945.
Meque, A. and Abiodun, B. J.: Simulating the link between ENSO and summer
drought in Southern Africa using regional climate models, Clim. Dynam., 44,
1881–1900, https://doi.org/10.1007/s00382-014-2143-3, 2015.
Munday, C. and Washington, R.: Circulation controls on southern African
precipitation in coupled models: The role of the Angola Low, J. Geophys.
Res. Atmospheres, 122, 861–877, https://doi.org/10.1002/2016JD025736, 2017.
Munday, C. and Washington, R.: Systematic Climate Model Rainfall Biases over
Southern Africa: Links to Moisture Circulation and Topography, J. Climate, 31,
7533–7548, https://doi.org/10.1175/JCLI-D-18-0008.1, 2018.
Nikulin, G., Jones, C., Giorgi, F., Asrar, G., Büchner, M., Cerezo-Mota,
R., Christensen, O. B., Déqué, M., Fernandez, J., Hänsler, A.,
Meijgaard, E. van, Samuelsson, P., Sylla, M. B., and Sushama, L.:
Precipitation Climatology in an Ensemble of CORDEX-Africa Regional Climate
Simulations, J. Climate, 25, 6057–6078,
https://doi.org/10.1175/JCLI-D-11-00375.1, 2012.
Novella, N. S. and Thiaw, W. M.: African Rainfall Climatology Version 2 for Famine Early Warning Systems, J. Appl. Meteorol. Clim., 52, 588–606, https://doi.org/10.1175/JAMC-D-11-0238.1, 2013.
Onyutha, C.: Trends and variability in African long-term precipitation,
Stoch. Environ. Res. Risk Ass., 32, 2721–2739,
https://doi.org/10.1007/s00477-018-1587-0, 2018.
Peterson, T. C. and Manton, M. J.: Monitoring Changes in Climate Extremes: A
Tale of International Collaboration, B. Am. Meteorol. Soc., 89,
1266–1271, 2008.
Pichelli, E., Coppola, E., Sobolowski, S., Ban, N., Giorgi, F., Stocchi, P.,
Alias, A., Belušić, D., Berthou, S., Caillaud, C., Cardoso, R. M.,
Chan, S., Christensen, O. B., Dobler, A., de Vries, H., Goergen, K., Kendon,
E. J., Keuler, K., Lenderink, G., Lorenz, T., Mishra, A. N., Panitz, H.-J.,
Schär, C., Soares, P. M. M., Truhetz, H., and Vergara-Temprado, J.: The
first multi-model ensemble of regional climate simulations at
kilometer-scale resolution part 2: historical and future simulations of
precipitation, Clim. Dynam., 56, 3581–3602, https://doi.org/10.1007/s00382-021-05657-4, 2021.
Pierce, D. W., Barnett, T. P., Santer, B. D., and Gleckler, P. J.: Selecting
global climate models for regional climate change studies, P. Natl. Acad.
Sci. USA, 106, 8441–8446, https://doi.org/10.1073/pnas.0900094106, 2009.
Pinto, I., Lennard, C., Tadross, M., Hewitson, B., Dosio, A., Nikulin, G.,
Panitz, H.-J., and Shongwe, M. E.: Evaluation and projections of extreme
precipitation over southern Africa from two CORDEX models, Climatic Change,
135, 655–668, https://doi.org/10.1007/s10584-015-1573-1, 2016.
R Core Team: A language and environment for statistical computing, R Foundation for Statistical Computing, https://www.r-project.org/, last access: 31 October 2021.
Reason, C. J. C. and Jagadheesha, D.: A model investigation of recent ENSO
impacts over southern Africa, Meteorol. Atmos. Phys., 89, 181–205,
https://doi.org/10.1007/s00703-005-0128-9, 2005.
Rocklöv, J. and Dubrow, R.: Climate change: an enduring challenge for
vector-borne disease prevention and control, Nat. Immunol., 21, 479–483,
https://doi.org/10.1038/s41590-020-0648-y, 2020.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and Ziese, M.: GPCC Full Data Reanalysis Version 7.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data, Global Precipitation Climatology Centre [data set], https://doi.org/10.5676/DWD_GPCC/FD_M_V7_050, 2015.
Schulzweida, U.: CDO User Guide (Version 2.0.0), https://code.mpimet.mpg.de/projects/cdo/, last access: 31 October 2021.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau,
J. Am. Stat. Assoc., 63, 1379–1389,
https://doi.org/10.1080/01621459.1968.10480934, 1968.
Serdeczny, O., Adams, S., Baarsch, F., Coumou, D., Robinson, A., Hare, W.,
Schaeffer, M., Perrette, M., and Reinhardt, J.: Climate change impacts in
Sub-Saharan Africa: from physical changes to their social repercussions,
Reg. Environ. Change, 17, 1585–1600,
https://doi.org/10.1007/s10113-015-0910-2, 2017.
Tarnavsky, E., Grimes, D., Maidment, R., Black, E., Allan, R. P., Stringer,
M., Chadwick, R., and Kayitakire, F.: Extension of the TAMSAT
Satellite-Based Rainfall Monitoring over Africa and from 1983 to Present, J.
Appl. Meteorol. Clim., 53, 2805–2822,
https://doi.org/10.1175/JAMC-D-14-0016.1, 2014.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and
the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression
Analysis, in: Henri Theil's Contributions to Economics and Econometrics:
Econometric Theory and Methodology, edited by: Raj, B. and Koerts, J.,
Springer Netherlands, Dordrecht, 345–381,
https://doi.org/10.1007/978-94-011-2546-8_20, 1992.
Tirado, M. C., Hunnes, D., Cohen, M. J., and Lartey, A.: Climate Change and
Nutrition in Africa, J. Hunger Environ. Nutr., 10, 22–46,
https://doi.org/10.1080/19320248.2014.908447, 2015.
Toté, C., Patricio, D., Boogaard, H., Van der Wijngaart, R., Tarnavsky,
E., and Funk, C.: Evaluation of Satellite Rainfall Estimates for Drought and
Flood Monitoring in Mozambique, Remote Sens., 7, 1758–1776,
https://doi.org/10.3390/rs70201758, 2015.
Ukkola, A. M., Kauwe, M. G. D., Roderick, M. L., Abramowitz, G., and Pitman,
A. J.: Robust Future Changes in Meteorological Drought in CMIP6 Projections
Despite Uncertainty in Precipitation, Geophys. Res. Lett., 47,
e2020GL087820, https://doi.org/10.1029/2020GL087820, 2020.
Warnatzsch, E. A. and Reay, D. S.: Temperature and precipitation change in
Malawi: Evaluation of CORDEX-Africa climate simulations for climate change
impact assessments and adaptation planning, Sci. Total Environ., 654,
378–392, https://doi.org/10.1016/j.scitotenv.2018.11.098, 2019.
Willmott, C. J. and Matsuura, K.: Smart Interpolation of Annually Averaged Air Temperature in the United States, J. Appl. Meteorol. 34, 2577–2586, https://doi.org/10.1175/1520-0450(1995)034<2577:SIOAAA>2.0.CO;2, 1995.
Wyser, K., van Noije, T., Yang, S., von Hardenberg, J., O'Donnell, D., and Döscher, R.: On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6, Geosci. Model Dev., 13, 3465–3474, https://doi.org/10.5194/gmd-13-3465-2020, 2020.
Xie, P. and Arkin, P. A.: Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite 269 Estimates, and Numerical Model Outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997.
https://doi.org/10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2
Yim, B. Y., Yeh, S.-W., and Sohn, B.-J.: ENSO-Related Precipitation and Its
Statistical Relationship with the Walker Circulation Trend in CMIP5 AMIP
Models, Atmosphere, 7, 19, https://doi.org/10.3390/atmos7020019, 2016.
Short summary
The region of southern Africa (SAF) is highly vulnerable to the impacts of climate change and is projected to experience severe precipitation shortages in the coming decades. Reliable climatic information is therefore necessary for the optimal adaptation of local communities. In this work we show that regional climate models are reliable tools for the simulation of precipitation over southern Africa. However, there is still a great need for the expansion and maintenance of observational data.
The region of southern Africa (SAF) is highly vulnerable to the impacts of climate change and is...
