Articles | Volume 13, issue 7
https://doi.org/10.5194/gmd-13-3179-2020
© Author(s) 2020. 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-13-3179-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
14 Jul 2020
Development and technical paper |
| 14 Jul 2020
| 14 Jul 2020
Evaluation of three new surface irrigation parameterizations in the WRF-ARW v3.8.1 model: the Po Valley (Italy) case study
Arianna Valmassoi
CORRESPONDING AUTHOR
School of Architecture, Planning and Environmental Policy, University College Dublin (UCD), Richview, Dublin, Ireland
National Center for Atmospheric Research, Boulder, Colorado, USA
Jimy Dudhia
National Center for Atmospheric Research, Boulder, Colorado, USA
Silvana Di Sabatino
Department of Physics and Astronomy, University of Bologna, Bologna, Italy
Francesco Pilla
School of Architecture, Planning and Environmental Policy, University College Dublin (UCD), Richview, Dublin, Ireland
Related authors
Arianna Valmassoi and Jan D. Keller
Adv. Sci. Res., 18, 41–49, https://doi.org/10.5194/asr-18-41-2021, https://doi.org/10.5194/asr-18-41-2021, 2021
Akinleye Folorunsho, Jimy Dudhia, John Sullivan, Paul Walter, James Flynn, Travis Griggs, Rebecca Sheesley, Sascha Usenko, Guillaume Gronoff, Mark Estes, and Yang Li
EGUsphere, https://doi.org/10.5194/egusphere-2024-1190, https://doi.org/10.5194/egusphere-2024-1190, 2024
Short summary
Short summary
Our study investigates the factors driving high ozone levels over the Houston urban area. Using advanced modeling techniques and real-world measurements, we found vehicle and industrial emissions especially of highly reactive organic compounds play a key role in ozone formation. Our study highlights spatial and temporal changes in ozone sensitivity and variability of atmosphere's self-cleaning capacity to emissions, signifying effective ways of controlling emissions to mitigate urban ozone.
Antonio Giordani, Michael Kunz, Kristopher M. Bedka, Heinz Jürgen Punge, Tiziana Paccagnella, Valentina Pavan, Ines M. L. Cerenzia, and Silvana Di Sabatino
Nat. Hazards Earth Syst. Sci., 24, 2331–2357, https://doi.org/10.5194/nhess-24-2331-2024, https://doi.org/10.5194/nhess-24-2331-2024, 2024
Short summary
Short summary
To improve the challenging representation of hazardous hailstorms, a proxy for hail frequency based on satellite detections, convective parameters from high-resolution reanalysis, and crowd-sourced reports is tested and presented. Hail likelihood peaks in mid-summer at 15:00 UTC over northern Italy and shows improved agreement with observations compared to previous estimates. By separating ambient signatures based on hail severity, enhanced appropriateness for large-hail occurrence is found.
Francesco Barbano, Erika Brattich, Carlo Cintolesi, Abdul Ghafoor Nizamani, Silvana Di Sabatino, Massimo Milelli, Esther E. M. Peerlings, Sjoerd Polder, Gert-Jan Steeneveld, and Antonio Parodi
Atmos. Meas. Tech., 17, 3255–3278, https://doi.org/10.5194/amt-17-3255-2024, https://doi.org/10.5194/amt-17-3255-2024, 2024
Short summary
Short summary
The characterization of the urban microclimate starts with atmospheric monitoring using a dense array of sensors to capture the spatial variations induced by the different morphology, land cover, and presence of vegetation. To provide a new sensor for this scope, this paper evaluates the outdoor performance of a commercial mobile sensor. The results mark the sensor's ability to capture the same atmospheric variability as the reference, making it a valid solution for atmospheric monitoring.
Ditsuhi Iskandaryan, Silvana Di Sabatino, Francisco Ramos, and Sergio Trilles
AGILE GIScience Ser., 3, 6, https://doi.org/10.5194/agile-giss-3-6-2022, https://doi.org/10.5194/agile-giss-3-6-2022, 2022
Laura Tositti, Erika Brattich, Claudio Cassardo, Pietro Morozzi, Alessandro Bracci, Angela Marinoni, Silvana Di Sabatino, Federico Porcù, and Alessandro Zappi
Atmos. Chem. Phys., 22, 4047–4073, https://doi.org/10.5194/acp-22-4047-2022, https://doi.org/10.5194/acp-22-4047-2022, 2022
Short summary
Short summary
We present a thorough investigation of an anomalous transport of mineral dust over a region renowned for excess airborne particulate matter, the Italian Po Valley, which occurred in late March 2021. Both the origin of this dust outbreak, which was localized in central Asia (i.e., the so-called Aralkum Desert), and the upstream synoptic conditions, investigated here in extreme detail using multiple integrated observations including in situ measurements and remote sensing, were atypical.
Arianna Valmassoi and Jan D. Keller
Adv. Sci. Res., 18, 41–49, https://doi.org/10.5194/asr-18-41-2021, https://doi.org/10.5194/asr-18-41-2021, 2021
Laddaporn Ruangpan, Zoran Vojinovic, Silvana Di Sabatino, Laura Sandra Leo, Vittoria Capobianco, Amy M. P. Oen, Michael E. McClain, and Elena Lopez-Gunn
Nat. Hazards Earth Syst. Sci., 20, 243–270, https://doi.org/10.5194/nhess-20-243-2020, https://doi.org/10.5194/nhess-20-243-2020, 2020
Short summary
Short summary
This article aims to provide a critical review of the literature and indicate some directions for future research based on the current knowledge gaps in the area of nature-based solutions (NBSs) for hydro-meteorological risk reduction. The final full analysis was performed on 146 closely related articles. A review showed that many advancements related to NBSs have been made to date, but there are still many challenges that will play an important role in extending knowledge in the coming years.
P. A. Jiménez and J. Dudhia
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-7-9063-2014, https://doi.org/10.5194/gmdd-7-9063-2014, 2014
Revised manuscript not accepted
Short summary
Short summary
In spite of the substantial observational evidence supporting a higher drag over shallow waters than over the open ocean, regional and global models widely use a single formulation valid for the open ocean. Results of this work indicate that adding the extra drag is necessary to reconcile model results with long term observations of the wind profile within the first 100 m of the atmosphere, being the first modeling evidence supporting the reported added drag over shallow waters.
J. A. Ruiz-Arias, J. Dudhia, and C. A. Gueymard
Geosci. Model Dev., 7, 1159–1174, https://doi.org/10.5194/gmd-7-1159-2014, https://doi.org/10.5194/gmd-7-1159-2014, 2014
J. A. Ruiz-Arias, J. Dudhia, C. A. Gueymard, and D. Pozo-Vázquez
Atmos. Chem. Phys., 13, 675–692, https://doi.org/10.5194/acp-13-675-2013, https://doi.org/10.5194/acp-13-675-2013, 2013
Related subject area
Atmospheric sciences
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Improving the EnSRF in the Community Inversion Framework: a case study with ICON-ART 2024.01
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
The MESSy DWARF (based on MESSy v2.55.2)
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
Development of the adjoint of the unified tropospheric–stratospheric chemistry extension (UCX) in GEOS-Chem adjoint v36
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
Short summary
Short summary
Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Short summary
The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
Short summary
Short summary
The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary
Short summary
We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
Short summary
Short summary
We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
Short summary
A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Short summary
Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
Short summary
Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
Short summary
Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Short summary
This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary
Short summary
Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
Short summary
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
Short summary
Short summary
This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
Short summary
Short summary
In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
Short summary
Short summary
The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
Short summary
Short summary
The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
Short summary
Short summary
Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
Short summary
Short summary
Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
Short summary
Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
Short summary
Short summary
Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
Short summary
Short summary
Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
Short summary
Short summary
RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
Short summary
We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
Short summary
Short summary
We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
Short summary
Short summary
Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-145, https://doi.org/10.5194/gmd-2024-145, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements in 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
Short summary
Short summary
Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
Short summary
Short summary
AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
Short summary
Short summary
Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Hilda Sandström and Patrick Rinke
EGUsphere, https://doi.org/10.48550/arXiv.2406.18171, https://doi.org/10.48550/arXiv.2406.18171, 2024
Short summary
Short summary
Machine learning has the potential to aid the identification organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning model in atmospheric sciences.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
Short summary
Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
Short summary
Short summary
The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
Short summary
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2197, https://doi.org/10.5194/egusphere-2024-2197, 2024
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a more efficient implementation of the serial and batch versions of the Ensemble Square Root Filter (EnSRF) algorithm in CIF.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
Short summary
A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary
Short summary
The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Short summary
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
Short summary
Short summary
This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
Short summary
Short summary
Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
Short summary
Short summary
We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
Short summary
Short summary
This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
Short summary
Short summary
Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
Short summary
Short summary
TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
Short summary
Short summary
Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Cited articles
Aegerter,
C., Wang, J., Ge, C., Irmak, S., Oglesby, R., Wardlow, B., Yang, H., You, J., Shulski, M.,
Aegerter, C., Wang, J., Ge, C., Irmak, S., Oglesby, R., Wardlow, B., Yang, H., You, J., and
Shulski, M.: Mesoscale Modeling of the Meteorological Impacts of Irrigation during the 2012
Central Plains Drought, J. Appl. Meteorol. Climatol., 56, 1259–1283,
https://doi.org/10.1175/JAMC-D-16-0292.1, 2017. a, b, c, d, e, f
ARPAE: Bollettino agroclimatico mensile,
Luglio 2015, Tech. rep., Arpae Servizio idro-meteo-clima, 2015a. a
Bavi, A., Kashkuli, H. A., Boroomand, S., Naseri, A.,
and Albaji, M.: Evaporation losses from sprinkler irrigation systems under various operating
conditions, J. Appl. Sci., 9, 597–600, https://doi.org/10.3923/jas.2009.597.600, 2009. a
Bin Abdullah, K.: Use of water and
land for food security and environmental sustainability, in: Irrigation and Drainage, vol. 55,
219–222, John Wiley and Sons, Ltd., available
https://doi.org/10.1002/ird.254, 2006. a, b
Bonfils, C. and Lobell, D.: Empirical evidence
for a recent slowdown in irrigation-induced cooling, P. Natl. Acad. Sci. USA, 104,
13582–13587, https://doi.org/10.1073/pnas.0700144104, 2007. a
Boucher, O., Myhre, G., and
Myhre, A.: Direct human influence of irrigation on atmospheric water vapour and climate,
Clim. Dynam., 22, 597–603, https://doi.org/10.1007/s00382-004-0402-4, 2004. a, b, c, d
Cook, B. I., Puma, M. J., and Krakauer,
N. Y.: Irrigation induced surface cooling in the context of modern and increased greenhouse gas
forcing, Clim. Dynam., 37, 1587–1600, https://doi.org/10.1007/s00382-010-0932-x, 2010. a, b, c
Cook,
B. I., Shukla, S. P., Puma, M. J., and Nazarenko, L. S.: Irrigation as an historical climate
forcing, Clim. Dynam., 44, 1715–1730, https://doi.org/10.1007/s00382-014-2204-7, 2015. a, b
Daccache,
A., Ciurana, J. S., Rodriguez Diaz, J. A., and Knox, J. W.: Water and energy footprint of
irrigated agriculture in the Mediterranean region, Environ. Res. Lett., 9, 124014,
https://doi.org/10.1088/1748-9326/9/12/124014, 2014. a
de Vrese, P. and
Hagemann, S.: Uncertainties in modelling the climate impact of irrigation, Clim. Dynam., 51,
2023–2038, https://doi.org/10.1007/s00382-017-3996-z, 2018. a
Deangelis, A., Dominguez, F., Fan, Y., Robock, A., Kustu, M. D., and
Robinson, D.: Evidence of enhanced precipitation due to irrigation over the Great Plains of the
United States, J. Geophys. Res.-Atmos., 115, D15115, https://doi.org/10.1029/2010JD013892, 2010. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars,
A. C., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer,
A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg,
P., Köhler, M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.: The
ERA-Interim reanalysis: Configuration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b
Della-Marta, P. M., Luterbacher, J., von Weissenfluh, H., Xoplaki, E.,
Brunet, M., and Wanner, H.: Summer heat waves over western Europe 1880–2003, their relationship
to large-scale forcings and predictability, Clim. Dynam., 29, 251–275,
https://doi.org/10.1007/s00382-007-0233-1, 2007. a
Douglas, E. M., Beltrán-Przekurat, A., Niyogi, D.,
Pielke, R. A., and Vörösmarty, C. J.: The impact of agricultural intensification and
irrigation on land-atmosphere interactions and Indian monsoon precipitation – A mesoscale
modeling perspective, Global Planet. Change, 67, 117–128,
https://doi.org/10.1016/j.gloplacha.2008.12.007, 2009. a
Ek, M. B.: Implementation of Noah land surface
model advances in the National Centers for Environmental Prediction operational mesoscale Eta
model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
Eurostat: Agricultural products, Publications Office
of the European Union, Luxembourg, https://doi.org/10.2785/45595, 2013. a
Fader, M., Shi,
S., von Bloh, W., Bondeau, A., and Cramer, W.: Mediterranean irrigation under climate change: more
efficient irrigation needed to compensate for increases in irrigation water requirements,
Hydrol. Earth Syst. Sci., 20, 953–973, https://doi.org/10.5194/hess-20-953-2016, 2016. a, b, c
García-Herrera, R., Díaz, J., Trigo, R. M.,
Luterbacher, J., and Fischer, E. M.: A Review of the European Summer Heat Wave of 2003,
Crit. Rev. Environ. Sci. Technol., 40, 267–306, https://doi.org/10.1080/10643380802238137, 2010. a
Giorgi, F. and Lionello, P.: Climate change
projections for the Mediterranean region, Global Planet. Change, 63, 90–104,
https://doi.org/10.1016/j.gloplacha.2007.09.005, 2008. a
Guimberteau, M., Laval, K., Perrier, A., and Polcher,
J.: Global effect of irrigation and its impact on the onset of the Indian summer monsoon,
Clim. Dynam., 39, 1329–1348, https://doi.org/10.1007/s00382-011-1252-5, 2012. a, b, c
Harding, K. J., Twine, T. E.,
and Lu, Y.: Effects of dynamic crop growth on the simulated precipitation response to
irrigation, Earth Interact., 19, 1–31, https://doi.org/10.1175/EI-D-15-0030.1, 2015. a
Hong, S.-Y., Lim, K.-S.,
Kim, J.-H., Lim, J.-O. J., and Dudhia, J.: WRF Single-Moment 6-Class Microphysics Scheme (WSM6),
Weather, 2010, 5–6, 2005. a
Hong, S.-Y., Noh, Y., and Dudhia, J.:
A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes,
Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
IPCC: Climate Change 2014 Impacts, Adaptation, and
Vulnerability Part A: Global and Sectoral Aspects, Working Group II Contribution to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, , 2014. a
Jägermeyr, J., Gerten, D., Heinke, J., Schaphoff, S.,
Kummu, M., and Lucht, W.: Water savings potentials of irrigation systems: global simulation of
processes and linkages, Hydrol. Earth Syst. Sci., 19, 3073–3091,
https://doi.org/10.5194/hess-19-3073-2015, 2015. a
Kioutsioukis, I., de Meij, A., Jakobs, H., Katragkou, E.,
Vinuesa, J. F., and Kazantzidis, A.: High resolution WRF ensemble forecasting for irrigation:
Multi-variable evaluation, Atmos. Res., 167, 156–174, https://doi.org/10.1016/j.atmosres.2015.07.015,
2016. a
Krakauer, N. Y., Puma, M. J., Cook, B. I.,
Gentine, P., and Nazarenko, L.: Ocean–atmosphere interactions modulate irrigation's climate
impacts, Earth Syst. Dynam., 7, 863–876, https://doi.org/10.5194/esd-7-863-2016, 2016. a
Kueppers, L. M., Snyder, M. A., Sloan, L. C., Cayan, D.,
Jin, J., Kanamaru, H., Kanamitsu, M., Miller, N. L., Tyree, M., Du, H., and Weare, B.: Seasonal
temperature responses to land-use change in the western United States, Global Planet. Change,
60, 250–264, https://doi.org/10.1016/j.gloplacha.2007.03.005, 2008. a, b
Lawston,
P. M., Santanello, J. A., Zaitchik, B. F., and Rodell, M.: Impact of Irrigation Methods on Land
Surface Model Spinup and Initialization of WRF Forecasts, J. Hydrometeorol., 16, 1135–1154,
https://doi.org/10.1175/jhm-d-14-0203.1, 2015. a
Lee, E., Sacks,
W. J., Chase, T. N., and Foley, J. A.: Simulated impacts of irrigation on the atmospheric
circulation over Asia, J. Geophys. Res.-Atmos., 116, 1–13, https://doi.org/10.1029/2010JD014740, 2011. a
Lobell, D., Bala, G., Mirin, A., Phillips, T., Maxwell, R., and Rotman, D.: Regional differences
in the influence of irrigation on climate, J. Climate, 22, 2248–2255,
https://doi.org/10.1175/2008JCLI2703.1, 2009. a
Lobell, D. B., Bala, G.,
Bonfils, C., and Duffy, P. B.: Potential bias of model projected greenhouse warming in irrigated
regions, Geophys. Res. Lett., 33, L13709, https://doi.org/10.1029/2006GL026770, 2006. a, b
Lobell, D. B., Bonfils, C. J., Kueppers, L. M., and Snyder, M. A.: Irrigation cooling effect on
temperature and heat index extremes, Geophys. Res. Lett., 35, L09705,
https://doi.org/10.1029/2008GL034145, 2008a. a, b
Lobell, D. B., Burke, M. B., Tebaldi, C., Mastrandrea, M. D., Falcon,
W. P., and Naylor, R. L.: Prioritizing climate change adaptation needs for food security in
2030, Science, 319, 607–610, https://doi.org/10.1126/science.1152339, 2008b. a
Lv, G., Kang, Y., Li, L., and Wan, S.:
Effect of irrigation methods on root development and profile soil water uptake in winter wheat,
Irrig. Sci., 28, 387–398, https://doi.org/10.1007/s00271-009-0200-1, 2010. a
Mooney, P. A., Mulligan., F. J.,
and Fealy, R.: Evaluation of the sensitivity of the weather research and forecasting model to
parameterization schemes for regional climates of europe over the period 1990-95, J. Climate, 26,
1002–1017, https://doi.org/10.1175/JCLI-D-11-00676.1, 2013. a
Nair, S.,
Johnson, J., and Wang, C.: Efficiency of irrigation water use: A review from the perspectives of
multiple disciplines, Agron. J., 105, 351–363, https://doi.org/10.2134/agronj2012.0421, 2013. a
National
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP GFS 0.25
Degree Global Forecast Grids Historical Archive, Research Data Archive at the National Center
for Atmospheric Research, Computational and Information Systems Laboratory, Tech. rep.,
https://doi.org/10.5065/D65D8PWK, 2015. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E., J, P., Levis, S.,
Swenson, S. C., Thornton, E., Feddema, J., Heald, C. L., Lamarque, J.-F., Niu, G.-y., Qian, T.,
Running, S., Sakaguchi, K., Yang, L., Zeng, X., and Zeng, X.: Technical Description of version
4.0 of the Community Land Model (CLM), available at:
National Center for Atmospheric Research, Boulder, CO, 2013. a, b, c
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D. L.: Simulating the Effects of Irrigation
over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data,
J. Hydrometeorol., 11, 171–184, https://doi.org/10.1175/2009jhm1116.1, 2009. a
Pielke, R. A. and Zeng, X.: Influence on severe storm
development of irrigated land, Natl. Weather Digest, 14, 16–17, 1989. a
Puma, M. J. and Cook, B. I.: Effects of irrigation on
global climate during the 20th century, J. Geophys. Res.-Atmos., 115, D16120,
https://doi.org/10.1029/2010JD014122, 2010. a, b
Qian, Y., Huang, M., Yang,
B., and Berg, L. K.: A Modeling Study of Irrigation Effects on Surface Fluxes and Land–Air–Cloud
Interactions in the Southern Great Plains, J. Hydrometeorol., 14, 700–721,
https://doi.org/10.1175/jhm-d-12-0134.1, 2013. a, b, c, d
Running, S., Mu, Q., and Zhao,
M.: MOD16A2 MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V006 [Data set],
NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD16A2.006, 2017. a, b
Saeed, F., Hagemann, S., and
Jacob, D.: Impact of irrigation on the South Asian summer monsoon, Geophys. Res. Lett., 36,
L20711, https://doi.org/10.1029/2009GL040625, 2009. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Seneviratne, S. I. and
Stöckli, R.: Climate Variability and Extremes during the Past 100 Years, Springer
Netherlands, Dordrecht, https://doi.org/10.1007/978-1-4020-6766-2, 2008. a
Siebert, S. and
Döll, P.: Quantifying blue and green virtual water contents in global crop production as
well as potential production losses without irrigation, J. Hydrol., 384, 198–217,
https://doi.org/10.1016/j.jhydrol.2009.07.031, 2010. a
Siebert, S.,
Henrich, V., Frenken, K., and Burke, J.: Global Map of Irrigation Areas version 5., Food and
Agriculture Organization of the United Nations,
2013. a
Skamarock, W., Klemp, J., Dudhi, J., Gill, D., Barker, D., Duda, M., Huang,
X.-Y., Wang, W., and Powers, J.: A Description of the Advanced Research WRF Version 3, Technical
Report, p. 113,
https://doi.org/10.5065/D6DZ069T, 2008. a, b, c
Sorooshian, S., Li,
J., Hsu, K.-l., and Gao, X.: How significant is the impact of irrigation on the local
hydroclimate in California's Central Valley? Comparison of model results with ground and
remote-sensing data, J. Geophys. Res., 116, D06102, https://doi.org/10.1029/2010JD014775, 2011. a, b, c
Sorooshian, S.,
Aghakouchak, A., and Li, J.: Influence of irrigation on land hydrological processes over
California, J. Geophys. Res.-Atmos., 119, 13137–13152, https://doi.org/10.1002/2014JD022232, 2014. a
Sridhar, V.: Tracking the Influence of Irrigation on Land
Surface Fluxes and Boundary Layer Climatology, J. Cont. Water Res. Educ., 152, 79–93,
https://doi.org/10.1111/j.1936-704X.2013.03170.x, 2013. a
Stergiou, I.,
Tagaris, E., and Sotiropoulou, R.-E. P.: Sensitivity Assessment of WRF Parameterizations over
Europe, Proceedings, 1, 119, https://doi.org/10.3390/ecas2017-04138, 2017. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., Lemone,
M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and
verification of the Unified Noah Land surface model in the WRF model, 16th conference on
numerical weather prediction,
pp. 11–15, 2004. a
Thiery, W., Davin, E. L., Lawrence, D. M., Hirsch, A. L., Hauser, M., and Seneviratne, S. I.:
Present-day irrigation mitigates heat extremes, J. Geophys. Res.-Atmos., 122, 1403–1422,
https://doi.org/10.1002/2016JD025740, 2017. a, b, c, d
Thornthwaite, C. W.: An Approach toward a Rational
Classification of Climate, Geograph. Rev., 38, 55, https://doi.org/10.2307/210739, 1948. a
Tuinenburg, O. A. and
de Vries, J. P. R.: Irrigation Patterns Resemble ERA-Interim Reanalysis Soil Moisture Additions,
Geophys. Res. Lett., 44, 10341–10348, https://doi.org/10.1002/2017GL074884, 2017. a, b
Valmassoi, A.: Development of three new surface irrigation
parameterizations in the WRF-ARW model: evaluation for the Po Valley (Italy) case study,
Mendeley Data, v2, https://doi.org/10.17632/t3b6rtccj9, 2019. a
Van Alfen, N. K.: Water: advanced irrigation technologies,
2nd Edn., San Diego, CA, USA, Elsevier, Vol. 5, 378–406, 2014. a
Wada, Y., Wisser, D., Eisner,
S., Flörke, M., Gerten, D., Haddeland, I., Hanasaki, N., Masaki, Y., Portmann, F. T.,
Stacke, T., Tessler, Z., and Schewe, J.: Multimodel projections and uncertainties of irrigation
water demand under climate change, Geophys. Res. Lett., 40, 4626–4632,
https://doi.org/10.1002/grl.50686, 2013. a
Wei, J., Dirmeyer,
P. A., Wisser, D., Bosilovich, M. G., and Mocko, D. M.: Where Does the Irrigation Water Go? An
Estimate of the Contribution of Irrigation to Precipitation Using MERRA, J. Hydrometeorol., 14,
275–289, https://doi.org/10.1175/JHM-D-12-079.1, 2013. a, b, c
Zampieri, M., Ceglar, A., Dentener, F., Dosio, A., Naumann, G., Van
Den Berg, M., and Toreti, A.: When will current climate extremes affecting maize production
become the norm?, Earth's Future, 7, 113–122, https://doi.org/10.1029/2018EF000995, 2019. a
Zhang, C. and Wang, Y.: Projected future
changes of tropical cyclone activity over the Western North and South Pacific in a 20-km-Mesh
regional climate model, J. Climate, 30, 5923–5941, https://doi.org/10.1175/JCLI-D-16-0597.1, 2017. a
Short summary
Irrigation affects the atmosphere and models are required to understand its full impact. However, there is no agreed procedure to describe irrigation within regional models. The present study introduces three new methods to integrate this process into the models and validates it for the Po Valley in northern Italy. All the tests done show that the results are improved with the new irrigation techniques when compared against some measures (e.g., temperature, potential evapotranspiration).
Irrigation affects the atmosphere and models are required to understand its full impact....
