Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3215-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3215-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
03 Jun 2021
Development and technical paper |
| 03 Jun 2021
| 03 Jun 2021
Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0)
Department of Environmental Sciences and Engineering, University of
North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Diana Ramírez-Mejía
Centre for Research in Environmental Geography, Universidad Nacional
Autónoma de México, Morelia, 58190, Mexico
Jared Bowden
Department of
Applied Ecology, North Carolina State University, Raleigh, NC 27695, USA
Adrian Ghilardi
Centre for Research in Environmental Geography, Universidad Nacional
Autónoma de México, Morelia, 58190, Mexico
J. Jason West
Department of Environmental Sciences and Engineering, University of
North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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J-F. Mas, A. Pérez Vega, and A. Ghilardi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 457–462, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-457-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-457-2023, 2023
Chi-Tsan Wang, Bok H. Baek, William Vizuete, Lawrence S. Engel, Jia Xing, Jaime Green, Marc Serre, Richard Strott, Jared Bowden, and Jung-Hun Woo
Earth Syst. Sci. Data, 15, 5261–5279, https://doi.org/10.5194/essd-15-5261-2023, https://doi.org/10.5194/essd-15-5261-2023, 2023
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Hazardous air pollutant (HAP) human exposure studies usually rely on local measurements or dispersion model methods, but those methods are limited under spatial and temporal conditions. We processed the US EPA emission data to simulate the hourly HAP emission patterns and applied the chemical transport model to simulate the HAP concentrations. The modeled HAP results exhibit good agreement (R is 0.75 and NMB is −5.6 %) with observational data.
Kai-Lan Chang, Owen R. Cooper, J. Jason West, Marc L. Serre, Martin G. Schultz, Meiyun Lin, Virginie Marécal, Béatrice Josse, Makoto Deushi, Kengo Sudo, Junhua Liu, and Christoph A. Keller
Geosci. Model Dev., 12, 955–978, https://doi.org/10.5194/gmd-12-955-2019, https://doi.org/10.5194/gmd-12-955-2019, 2019
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We developed a new method for combining surface ozone observations from thousands of monitoring sites worldwide with the output from multiple atmospheric chemistry models. The result is a global surface ozone distribution with greater accuracy than any single model can achieve. We focused on an ozone metric relevant to human mortality caused by long-term ozone exposure. Our method can be applied to studies that quantify the impacts of ozone on human health and mortality.
Christopher G. Nolte, Tanya L. Spero, Jared H. Bowden, Megan S. Mallard, and Patrick D. Dolwick
Atmos. Chem. Phys., 18, 15471–15489, https://doi.org/10.5194/acp-18-15471-2018, https://doi.org/10.5194/acp-18-15471-2018, 2018
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Changes in air pollution in the United States are simulated under three near-future climate scenarios. Widespread increases in average ozone levels are projected, with the largest increases during summer under the highest warming scenario. Increases are driven by higher temperatures and emissions from vegetation and are magnified at the upper end of the ozone distribution. The increases in ozone have potentially important implications for efforts to protect human health.
Yuqiang Zhang, J. Jason West, Rohit Mathur, Jia Xing, Christian Hogrefe, Shawn J. Roselle, Jesse O. Bash, Jonathan E. Pleim, Chuen-Meei Gan, and David C. Wong
Atmos. Chem. Phys., 18, 15003–15016, https://doi.org/10.5194/acp-18-15003-2018, https://doi.org/10.5194/acp-18-15003-2018, 2018
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Here we use a fine-resolution (36 km) self-consistent 21-year air quality simulation from 1990 to 2010, a health impact function, and annual county-level population and baseline mortality rate estimates to estimate annual mortality burdens from PM2.5 and O3 in the US, and also the contributions to the trends. We found that the PM2.5-related mortality burden has steadily decreased by 53 %, while the O3-related mortality burden has increased by 13 %, with larger inter-annual variabilities.
Ciao-Kai Liang, J. Jason West, Raquel A. Silva, Huisheng Bian, Mian Chin, Yanko Davila, Frank J. Dentener, Louisa Emmons, Johannes Flemming, Gerd Folberth, Daven Henze, Ulas Im, Jan Eiof Jonson, Terry J. Keating, Tom Kucsera, Allen Lenzen, Meiyun Lin, Marianne Tronstad Lund, Xiaohua Pan, Rokjin J. Park, R. Bradley Pierce, Takashi Sekiya, Kengo Sudo, and Toshihiko Takemura
Atmos. Chem. Phys., 18, 10497–10520, https://doi.org/10.5194/acp-18-10497-2018, https://doi.org/10.5194/acp-18-10497-2018, 2018
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Emissions from one continent affect air quality and health elsewhere. Here we quantify the effects of intercontinental PM2.5 and ozone transport on human health using a new multi-model ensemble, evaluating the health effects of emissions from six world regions and three emission source sectors. Emissions from one region have significant health impacts outside of that source region; similarly, foreign emissions contribute significantly to air-pollution-related deaths in several world regions.
Ulas Im, Jørgen Brandt, Camilla Geels, Kaj Mantzius Hansen, Jesper Heile Christensen, Mikael Skou Andersen, Efisio Solazzo, Ioannis Kioutsioukis, Ummugulsum Alyuz, Alessandra Balzarini, Rocio Baro, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Augustin Colette, Gabriele Curci, Aidan Farrow, Johannes Flemming, Andrea Fraser, Pedro Jimenez-Guerrero, Nutthida Kitwiroon, Ciao-Kai Liang, Uarporn Nopmongcol, Guido Pirovano, Luca Pozzoli, Marje Prank, Rebecca Rose, Ranjeet Sokhi, Paolo Tuccella, Alper Unal, Marta Garcia Vivanco, Jason West, Greg Yarwood, Christian Hogrefe, and Stefano Galmarini
Atmos. Chem. Phys., 18, 5967–5989, https://doi.org/10.5194/acp-18-5967-2018, https://doi.org/10.5194/acp-18-5967-2018, 2018
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The impacts of air pollution on human health and their costs in Europe and the United States for the year 2010 ared modeled by a multi-model ensemble. In Europe, the number of premature deaths is calculated to be 414 000, while in the US it is estimated to be 160 000. Health impacts estimated by individual models can vary up to a factor of 3. Results show that the domestic emissions have the largest impact on premature deaths, compared to foreign sources.
Raquel A. Silva, J. Jason West, Jean-François Lamarque, Drew T. Shindell, William J. Collins, Stig Dalsoren, Greg Faluvegi, Gerd Folberth, Larry W. Horowitz, Tatsuya Nagashima, Vaishali Naik, Steven T. Rumbold, Kengo Sudo, Toshihiko Takemura, Daniel Bergmann, Philip Cameron-Smith, Irene Cionni, Ruth M. Doherty, Veronika Eyring, Beatrice Josse, Ian A. MacKenzie, David Plummer, Mattia Righi, David S. Stevenson, Sarah Strode, Sophie Szopa, and Guang Zengast
Atmos. Chem. Phys., 16, 9847–9862, https://doi.org/10.5194/acp-16-9847-2016, https://doi.org/10.5194/acp-16-9847-2016, 2016
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Using ozone and PM2.5 concentrations from the ACCMIP ensemble of chemistry-climate models for the four Representative Concentration Pathway scenarios (RCPs), together with projections of future population and baseline mortality rates, we quantify the human premature mortality impacts of future ambient air pollution in 2030, 2050 and 2100, relative to 2000 concentrations. We also estimate the global mortality burden of ozone and PM2.5 in 2000 and each future period.
Yuqiang Zhang, Jared H. Bowden, Zachariah Adelman, Vaishali Naik, Larry W. Horowitz, Steven J. Smith, and J. Jason West
Atmos. Chem. Phys., 16, 9533–9548, https://doi.org/10.5194/acp-16-9533-2016, https://doi.org/10.5194/acp-16-9533-2016, 2016
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Reducing greenhouse gas (GHG) emissions can also improve air quality. We estimate the co-benefits of global GHG mitigation for US air quality in 2050 at fine resolution by downscaling from a previous global study. Foreign GHG mitigation under RCP4.5 contributes more to the US O3 reduction (76 % of the total) than domestic mitigation and contributes 26 % of the PM2.5 reduction. Therefore, the US gains significantly greater air quality co-benefits by coordinating GHG controls internationally.
Y. Gao, A. Ghilardi, J. F. Mas, J. Paneque-Galvez, and M. Skutsch
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 9–13, https://doi.org/10.5194/isprs-archives-XLI-B2-9-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-9-2016, 2016
M. C. Woody, J. J. West, S. H. Jathar, A. L. Robinson, and S. Arunachalam
Atmos. Chem. Phys., 15, 6929–6942, https://doi.org/10.5194/acp-15-6929-2015, https://doi.org/10.5194/acp-15-6929-2015, 2015
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Utilizing an aircraft-specific parameterization based on smog chamber data in a regional AQM, contributions of non-traditional secondary organic aerosols (NTSOA) from aircraft emissions of semi-volatile and intermediate volatility organic compounds were assessed. NTSOA, a previously unaccounted component of PM2.5 in most AQMs, contributed up to 7.4% of aviation-attributable PM2.5 at the airport and rose to 17.9% downwind, suggesting its significance in aviation-attributed PM2.5 at all scales.
M. M. Fry, M. D. Schwarzkopf, Z. Adelman, and J. J. West
Atmos. Chem. Phys., 14, 523–535, https://doi.org/10.5194/acp-14-523-2014, https://doi.org/10.5194/acp-14-523-2014, 2014
J. Rissman, S. Arunachalam, M. Woody, J. J. West, T. BenDor, and F. S. Binkowski
Atmos. Chem. Phys., 13, 9285–9302, https://doi.org/10.5194/acp-13-9285-2013, https://doi.org/10.5194/acp-13-9285-2013, 2013
M. M. Fry, M. D. Schwarzkopf, Z. Adelman, V. Naik, W. J. Collins, and J. J. West
Atmos. Chem. Phys., 13, 5381–5399, https://doi.org/10.5194/acp-13-5381-2013, https://doi.org/10.5194/acp-13-5381-2013, 2013
W. J. Collins, M. M. Fry, H. Yu, J. S. Fuglestvedt, D. T. Shindell, and J. J. West
Atmos. Chem. Phys., 13, 2471–2485, https://doi.org/10.5194/acp-13-2471-2013, https://doi.org/10.5194/acp-13-2471-2013, 2013
Related subject area
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AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
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A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
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TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
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The CHIMERE chemistry-transport model v2023r1
tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Merged Observatory Data Files (MODFs): an integrated observational data product supporting process-oriented investigations and diagnostics
Simulation of marine stratocumulus using the super-droplet method: numerical convergence and comparison to a double-moment bulk scheme using SCALE-SDM 5.2.6-2.3.1
WRF-Comfort: simulating microscale variability in outdoor heat stress at the city scale with a mesoscale model
Representing effects of surface heterogeneity in a multi-plume eddy diffusivity mass flux boundary layer parameterization
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RoadSurf 1.1: open-source road weather model library
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
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Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
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An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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.
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
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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.
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
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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
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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
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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.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
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We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
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A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 2024
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Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate in the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
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In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
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A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
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Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
Nathan P. Arnold
Geosci. Model Dev., 17, 5041–5056, https://doi.org/10.5194/gmd-17-5041-2024, https://doi.org/10.5194/gmd-17-5041-2024, 2024
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Earth system models often represent the land surface at smaller scales than the atmosphere, but surface–atmosphere coupling uses only aggregated surface properties. This study presents a method to allow heterogeneous surface properties to modify boundary layer updrafts. The method is tested in single column experiments. Updraft properties are found to reasonably covary with surface conditions, and simulated boundary layer variability is enhanced over more heterogeneous land surfaces.
Enrico Dammers, Janot Tokaya, Christian Mielke, Kevin Hausmann, Debora Griffin, Chris McLinden, Henk Eskes, and Renske Timmermans
Geosci. Model Dev., 17, 4983–5007, https://doi.org/10.5194/gmd-17-4983-2024, https://doi.org/10.5194/gmd-17-4983-2024, 2024
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Nitrogen dioxide (NOx) is produced by sources such as industry and traffic and is directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time-consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study, we apply a consistent methodology to derive NOx emissions over Germany and illustrate the value of having such a method for fast projections.
Yuhan Xu, Sheng Fang, Xinwen Dong, and Shuhan Zhuang
Geosci. Model Dev., 17, 4961–4982, https://doi.org/10.5194/gmd-17-4961-2024, https://doi.org/10.5194/gmd-17-4961-2024, 2024
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Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
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Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
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RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
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The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
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We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
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The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
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By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
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We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
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The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
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The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
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There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
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We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
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This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
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Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
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In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Álvaro González-Cervera and Luis Durán
EGUsphere, https://doi.org/10.5194/egusphere-2024-958, https://doi.org/10.5194/egusphere-2024-958, 2024
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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 of broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-84, https://doi.org/10.5194/gmd-2024-84, 2024
Revised manuscript accepted for GMD
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We apply a comprehensive approach to select a subset of CMIP6 that is suitable for dynamical downscaling over Southeast Asia by considering model performance, model independence, data availability, and future climate change spread. The standardised benchmarking framework is applied to identify a subset of models through two stages of assessment: statistical-based and process-based metrics. We finalize a sub-set of two independent models for dynamical downscaling over Southeast Asia.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Cited articles
Abiodun, B. J., Pal, J. S., Afiesimama, E. A., Gutowski, W. J., and
Adedoyin, A.: Simulation of West African monsoon using REgCM3 Part II:
impacts of deforestation and desertification, Theor. Appl. Climatol., 93,
245–261, https://doi.org/10.1007/s00704-007-0333-1, 2008.
Adeniyi, M. O. and Dilau, K. A.: Assessing the link between Atlantic Nino 1
and drought over Africa using CORDEX regional climate models, Theor. Appl.
Climatol., 131, 937–949, https://doi.org/10.1007/s00704-016-2018-0, 2018.
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979–Present), J. Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
Alaka, G. J. and Maloney, E. D.: Internal intraseasonal variability of the
West African Monsoon in WRF, J. Climate, 30, 5815–5832,
https://doi.org/10.1175/JCLI-D-16-0750.1, 2017.
Argent, R., Sun, X., Semazzi, F., Xie, L., and Liu, B.: The development of a
customization framework for the WRF model of the Lake Victoria Basin,
Eastern Africa on Seasonal Timescales, Adv. Meteorol., 2015, 653473, https://doi.org/10.1155/2015/653473, 2015.
Arnault, J., Knoche, R., Wei, J., and Kuntsmann, H.: Evaporation tagging and
atmospheric water budget analysis with WRF: A regional precipitation
recycling study for West Africa, Water Resour. Res., 52, 1544–1567,
https://doi.org/10.1002/2015WR017704, 2016.
Boisier, J. P., de Noblet-Ducoudre, N., Pitman, A. J., Cruz, F. T., Delire,
C., van den Hurk, B. J. J. M., van der Molen, M. K., Muller, C., and
Voldoire, A.: Attributing the impacts of land-cover changes in temperate
regions on surface temperature and heat fluxes to specific causes: Results
from the first LUCID set of simulations, J. Geophys. Res.-Atmos., 117, D12116, https://doi.org/10.1029/2011JD017106, 2012.
Boisier, J. P., de Noblet-Ducoudré, N., and Ciais, P.: Inferring past land use-induced changes in surface albedo from satellite observations: a useful tool to evaluate model simulations, Biogeosciences, 10, 1501–1516, https://doi.org/10.5194/bg-10-1501-2013, 2013.
Boone, A. A., Xue, Y., De Salesm, F., Comer, R. E., Hagos, S., Mahanama, S.,
Schiro, K., Song, G., Wang, G., Li, S., and Mechoso, C. R.: The regional
impact of land-use land-cover change (LULCC) over West Africa from and
ensemble of global climate models under the auspices of the WAMME2 project,
Clim. Dynam., 47, 3547–3573, https://doi.org/10.1007/s00382-016-3252-y, 2016.
Boulard, D., Pohl, B., Cretat, J., Vigaud, N., and Pham-Xuan, T.:
Downscaling large-scale climate variability using a regional climate model:
the case of ENSO over Southern Africa, Clim. Dynam., 40, 1141–1168,
https://doi.org/10.1007/s00382-012-1400-6, 2013.
Bowman, M. S., Soares-Filho, B. S., Merry, F. D., Nepstad, D. C., Rodrigues,
H. O., and Almeida, O. T.: Persistence of cattle ranching in the Brazilian
Amazon: A spatial analysis of the rationale for beef production,
Land Use Policy, 29, 558–568, https://doi.org/10.1016/j.landusepol.2011.09.009, 2012.
Boysen, L. R., Brovkin, V., Arora, V. K., Cadule, P., de Noblet-Ducoudré, N., Kato, E., Pongratz, J., and Gayler, V.: Global and regional effects of land-use change on climate in 21st century simulations with interactive carbon cycle, Earth Syst. Dynam., 5, 309–319, https://doi.org/10.5194/esd-5-309-2014, 2014.
Breil, M., Rechid, D., Davin, E. L., de Noblet-Ducoudré, N., Katragkou,
E., Cardoso, R. M., Hoffmann, P., Jach, L. L., Soares, P. M. M., Sofiadis,
G., Strada, S., Strandberg, G., Tölle, M. H., and Warrach-Sagi, K.: The
Opposing Effects of Reforestation and Afforestation on the Diurnal
Temperature Cycle at the Surface and in the Lowest Atmospheric Model Level
in the European Summer, J. Climate, 33, 9159–9179,
https://doi.org/10.1175/JCLI-D-19-0624.1, 2020.
Bright, R. M.: Metrics for biogeophysical climate forcings from land use and
land cover Changes and their inclusion in life cycle assessment: A critical
review, Environ. Sci. Technol., 49, 3291–3303, https://doi.org/10.1021/es505465t, 2015.
Bright, R. M., Eisner, S., Lund, M. T., Majasalmi, T., Myhre, G., and
Astrup, R.: Inferring surface albedo prediction error linked to forest
structure at high latitudes, J. Geophys. Res.-Atmos., 123, 4910–4925,
https://doi.org/10.1029/2018JD028293, 2018.
Burakowski, E. A., Bonan, S. V., Wake, G. B., Dibb, C. P., and Hollinger, J.
E.: Evaluating the climate effects of reforestation in New England using a
Weather Research and Forecasting (WRF) model multiphysics ensemble, J. Climate, 29, 5141–5156, https://doi.org/10.1175/JCLI-D-15-0286.1, 2016.
Carlson, K. M., Curran, L. M., Ratnasari, D., Pittman, A. M., Soares-Filho,
B. S., Asner, G. P., Trigg, S. N., Gaveau, D. A., Lawrence, D., and
Rodrigues, H. O.: Committed carbon emissions, deforestation, and community
land conversion from oil palm plantation expansion in West Kalimantan,
Indonesia, P. Natl. Acad. Sci. USA, 109, 7559–7564, https://doi.org/10.1073/pnas.1200452109, 2012.
Charney, J. G.: Dynamics of deserts and drought in the Sahel,
Q. J. Roy. Meteor. Soc., 101, 193–202, https://doi.org/10.1002/qj.49710142802, 1975.
Chen, F. and Dudhia, J.: Coupling an advanced land-surface/hydrology model
with the Penn state/NCAR MM5 modeling system, Part I: model description and
implementation, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Cheng, L. L., Liu, M., and Zhan, J. Q.: Land use scenario simulation of
mountainous districts based on Dinamica EGO model, J. Mt. Sci.-Engl., 17,
289–303, https://doi.org/10.1007/s11629-019-5491-y, 2020.
Clough, S. A., Shephard, M. W., Mlawer, J. E., Delamere, J. S., Iacono, M.
J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative
transfer modeling: a summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005.
Collier, P., Conway, G., and Venables, T.: Climate change and Africa,
Oxford Rev. Econ. Pol., 24, 337–353, https://doi.org/10.1093/oxrep/grn019, 2008.
Cook, C., Reason, C. J. C., and Hewitson, B. C.: Wet and dry spells within
particularly wet and dry summers in the South African summer rainfall
region, Climate Res., 26, 17–31, https://doi.org/10.3354/cr026017, 2004.
Cretat, J., Pohl, B., Richard, Y., and Drobinski, P.: Uncertainties in
simulating regional climate of Southern Africa: sensitivity to physical
parameterizations using WRF, Clim. Dynam., 38, 613–634,
https://doi.org/10.1007/s00382-011-1055-8, 2012.
Cretat, J., Pohl, B., Dieppois, B., Berthou, S., and Pergaud, J.: The Angola
Low: relationship with southern Africa rainfall and ENSO, Clim. Dynam., 52,
1783–1803, https://doi.org/10.1007/s00382-018-4222-3, 2019.
Crossley, J. F., Polcher, J., Cox, P. M., Gedney, N., and Planton, S.:
Uncertainties linked to land-surface processes in climate change
simulations, Clim. Dynam., 16, 949–961, https://doi.org/10.1007/s003820000092, 2000.
De Almeida, C. M., Monteiro, A. M. V., Soares, G. C. B. S., Cerqueira, G.
C., Pennachin, C. L., and Batty, M.: GIS and remote sensing as tools for the
simulation of urban land-use change, Int. J. Remote Sens., 26, 759–774,
https://doi.org/10.1080/01431160512331316865, 2005.
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. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, 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.
Diasso, U. and Abiodun, B. J.: Drought modes in West Africa and how well
CORDEX RCMs simulate them, Theor. Appl. Climatol., 128, 223–240,
https://doi.org/10.1007/s00704-015-1705-6, 2017.
Diaz, J. P., Gonzalez, A., Exposito, F. J., Perez, J. C., Fernandez, J.,
Garcia-Diez, M., and Taima, D.: WRF multi-physics simulation of clouds in
the African region, Q. J. Roy. Meteor. Soc., 141, 2737–2749,
https://doi.org/10.1002/qj.2560, 2015.
Dlugokencky, E. and Tans, P.: Carbon Cycle Greenhouse Gases, NOAA/GML, available at: https://gml.noaa.gov/ccgg/trends/, last access: 5 September 2018.
Duveiller, G., Hooker, J., and Cescatti, A.: The mark of vegetation change
on Earth's surface energy balance, Nat. Commun., 9, 679,
https://doi.org/10.1038/s41467-017-02810-8, 2018.
Ek, M., Mitchell, K., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno,
G., and Tarpley, J.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Endris, H. S., Lennard, C., Hewitson, B., Dosio, A., Nikulin, G., and
Panitz, H.-J.: Teleconnection responses in multi-GCM driven CORDEX RCMs over
Eastern Africa, Clim. Dynam., 46, 2821–2846,
https://doi.org/10.1007/s00382-015-2734-7, 2016.
European Centre for Medium-Range Weather Forecasts (ECMWF): ERA-Interim Project, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, https://doi.org/10.5065/D6CR5RD9 (last access: 5 October 2018), 2009.
Fita, L., Polcher, J., Giannaros, T. M., Lorenz, T., Milovac, J., Sofiadis, G., Katragkou, E., and Bastin, S.: CORDEX-WRF v1.3: development of a module for the Weather Research and Forecasting (WRF) model to support the CORDEX community, Geosci. Model Dev., 12, 1029–1066, https://doi.org/10.5194/gmd-12-1029-2019, 2019.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover
Type Yearly L3 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC,
USA, https://doi.org/10.5067/MODIS/MCD12Q1.006, 2015.
Friedl, M., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D.,
Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A.,
Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS:
algorithms and early results, Remote Sens. Environ., 83, 287–302,
https://doi.org/10.1016/S0034-4257(02)00078-0, 2002.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X. M.: MODIS Collection 5 global land cover:
Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Gbobaniyi, E., Sarr, A., Sylla, M. B., Diallo, I., Lennard, C., Dosio, A.,
Dhiediou, A., Kamga, A., Klutse, N. A. B., Hewitson, B., Nikulin, G., and
Lamptey, B.: Climatology, annual cycle and interannual variability of
precipitation and temperature in CORDEX simulations over West Africa, Int.
J. Climatol., 34, 2241–2257, https://doi.org/10.1002/joc.3834, 2014.
Ge, J., Qi, J., Lofgren, B. M., Moore, N., Torbick, N., and Olson, J. M.:
Impacts of land use/cover classification accuracy on regional climate
simulations, J. Geophys. Res.-Atmos., 112, D05107,
https://doi.org/10.1029/2006JD007404, 2007.
Ge, J., Qi, J., and Lofgren, B.: Use of vegetation properties from EOS
observations for land-climate modeling in East Africa, J. Geophys. Res.-Atmos., 113, D15101, https://doi.org/10.1029/2007JD009628, 2008.
Ghilardi, A., Bailis, R., Mas, J. F., Skutsch, M., Elvir, J. A., Quevedo,
A., Masera, O., Dwivedi, P., Drigo, R., and Vega, E.: Spatiotemporal
modeling of fuelwood environmental impacts: Towards improved accounting for
non-renewable biomass, Environ. Modell. Softw., 82, 241–254,
https://doi.org/10.1016/j.envsoft.2016.04.023, 2016.
Gilliam, G., Pleim, J., and Xiu, A.: Implementation of the Pleim-Xiu Land
Surface Model and Asymmetric Convective Model in the WRF Model, in: 8th Annual WRF User's Workshop, Boulder, Colorado, USA, 11–15 June 2007.
Glotfelty, T., Ramirez, D., Bowden, J., Ghilardi, A., and West, J. J.: CLM-AF v 1.0 Code, UNC Dataverse, V1 [code], https://doi.org/10.15139/S3/DZ7XS3, 2020a.
Glotfelty, T., Ramirez, D., Bowden, J., Ghilardi, A., and West, J. J.: CLM-AF Updated Radiation Codes, UNC Dataverse, V1 [code], https://doi.org/10.15139/S3/W2LWJV, 2020b.
Glotfelty, T., Ramirez, D., Bowden, J., Ghilardi, A., and West, J. J.: Default WRF-CLM LAI Output Code, UNC Dataverse, V1 [code], https://doi.org/10.15139/S3/JGIQOE, 2020c.
Glotfelty, T., Ramirez, D., Bowden, J., Ghilardi, A., and West, J. J.: Africa-Bioclimate Regions, UNC Dataverse, V1 [data set], https://doi.org/10.15139/S3/WHNILT, 2020d.
Glotfelty, T., Ramirez, D., Bowden, J., Ghilardi, A., and West, J. J.: MODIS DinamicaEGO Land Use Data, UNC Dataverse, V1 [data set], https://doi.org/10.15139/S3/BEA55Z, 2020e.
Glotfelty, T., Ramirez, D., Bowden, J., Ghilardi, A., and West, J. J.: Overview Information, UNC Dataverse, V1 [data set], https://doi.org/10.15139/S3/MQ8KNS, 2020f.
Gu, H., Jin, J., Wu, Y., Ek, M. B., and Subin, Z. M.: Calibration and
validation of lake surface temperature simulations with the coupled WRF-lake
model, Climate Change, 129, 471–483, https://doi.org/10.1007/s10584-013-0978-y, 2015.
Hagen, A.: Fuzzy set approach to assessing similarity of categorical maps,
Int. J. Geogr. Inf. Sci., 17, 235–249,
https://doi.org/10.1080/13658810210157822, 2003.
Hagos, S., Leung, L. R., Xue, Y., Boone, A., de Sales, F., Neupane, N.,
Huang, M., and Yoon, J.-H.: Assessment of uncertainties in the response of
the African monsoon precipitation to land use change simulated by a regional
model, Clim. Dynam., 43, 2765–2775, https://doi.org/10.1007/s00382-014-2092-x,
2014.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711,
2014.
Harris, I. C. and Jones, P. D.: CRU TS4.01: Climatic Research Unit (CRU) Time-Series (TS) version 4.01 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901–Dec. 2016), Centre for Environmental Data Analysis, 4 December 2017 [data set], https://doi.org/10.5285/58a8802721c94c66ae45c3baa4d814d0 (last access: 13 November 2020), 2017.
Hartley, A. J., MacBean, N., Georgievski, G., and Bontemps, S.: Uncertainty
in plant functional type distributions and its impact on and surface models,
Remote Sens. Environ., 203, 71–89, https://doi.org/10.1016/j.rse.2017.07.037, 2017.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long–lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Igri, P. M., Tanessong, R. S., Vondou, D. A., Panda, J., Garba, A., Mkankam,
F. K., and Kamga, A.: Assessing the performance of the WRF model in
predicting high-impact weather conditions over Central and Western Africa:
an ensemble-based approach, Nat. Hazards, 93, 1565–1587,
https://doi.org/10.1007/s11069-018-3368-y, 2018.
Jin, J. and Wen, L.: Evaluation of snowmelt simulations in the Weather
Research and Forecasting Model, J. Geophys. Res.-Atmos., 117, D10110,
https://doi.org/10.1029/2011JD016980, 2012.
Kang, H.-S., Xue, Y., and Collatz, G. J.: Impact assessment of satellite-derived
lead area index datasets using a general circulation model, J. Climate, 20,
993–1015, https://doi.org/10.1175/JCLI4054.1, 2007.
Karri, S., Gharai, B., Sai Krishna, S. V. S., and Rao, P. V. N.: Impact of
AWiFS derived land cover on simulation of heavy rainfall, in: Proc. SPIE 9882, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI, SPIE Asia-Pacific Remote Sensing, New Delhi, India, 3 May 2016, 98821M,
https://doi.org/10.1117/12.2223627, 2016.
Kerandi, N. M., Laux, P., Arnault, J., and Kunstmann, H.: Performance of the
WRF model to simulate the seasonal and interannual variability of
hydrometeorological variables in East Africa: a case study for the Tana
River basin in Kenya, Theor. Appl. Climatol., 130, 401–418,
https://doi.org/10.1007/s00704-016-1890-y, 2017.
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 Farve, 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.
Klein, C., Heinzeller, D., Bliefernicht, J., and Kunstmann, H.: Variability
of West African monsoon patterns generated by a WRF multi-physics ensemble,
Clim. Dynam., 45, 2733–2755, https://doi.org/10.1007/s00382-015-2505-5, 2015.
Klein, C., Bliefernicht, J., Heinzeller, D., Gessner, U., Klein, I., and
Kunstmann, H.: Feedback of observed interannual vegetation change: a
regional climate model analysis for the West African monsoon, Clim. Dynam.,
48, 2837–2858, https://doi.org/10.1007/s00382-016-3237-x, 2017.
Lamptey, B. L., Barron, E. J., and Pollard, D.: Simulation of the relative
impact of land cover and carbon dioxide to climate change from 1700 to 2100,
J. Geophys. Res.-Atmos., 110, D20103, https://doi.org/10.1029/2005JD005916, 2005.
Lauer, A. and Hamilton, K.: Simulating clouds with global climate models: a
comparison of CMIP5 results with CMIP3 and satellite data, J. Climate, 26,
3833–3845, https://doi.org/10.1175/JCLI-D-12-00451.1, 2013.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land
surface in the Community Land Model (CLM 3.0), J. Geophys. Res.-Biogeo., 112,
G01023, https://doi.org/10.1029/2006JG000168, 2007.
Lawrence, P. J. and Chase, T. N.: Climate impacts of making
evapotranspiration in the Community Land Model (CLM3) consistent with the
Simple Biosphere Model (SiB), J. Hydrometeorol., 10, 374–394,
https://doi.org/10.1175/2008JHM987.1, 2009.
Lejeune, Q., Seneviratne, S. I., and Davin, E. L.: Historical land-cover
change impacts on climate: Comparative assessment of LUCID and CMIP5
multimodel experiments, J. Climate, 30, 1439–1459,
https://doi.org/10.1175/JCLI-D-16-0213.1, 2017.
Li, R., Wang, S.-Y., and Gillies, R. R.: Significant impacts of radiation physics in the Weather Research and Forecasting model on the precipitation and dynamics of the West African Monsoon, Clim. Dynam., 44, 1583–1594, https://doi.org/10.1007/s00382-014-2294-2, 2015.
Longobardi, P., Montenegro, A., Beltrami, H., and Eby, M.: Deforestation
induced climate change: Effects of spatial scale, PLoS ONE, 11, e0153357,
https://doi.org/10.1371/journal.pone.0153357, 2016.
Lu, L. and Shuttleworth, W. J.: Incorporating NDVI-Derived LAI into the
climate versions of RAMS and its impact on regional climate, J.
Hydrometeorol., 3, 347–362, https://doi.org/10.1175/1525-7541(2002)003<0347:INDLIT>2.0.CO;2, 2002.
Lu, Y. and Kueppers, L. M.: Surface energy partitioning over four dominant
vegetation types across the United States in a coupled regional climate
model (Weather Research and Forecasting Model 3-Community Land Model 3.5),
J. Geophys. Res.-Atmos., 117, D06111, https://doi.org/10.1029/2011JD016991, 2012.
Mahmood, R., Pielke, R. A., Hubbard, K. G., Niyogi, D., Dirmeyer, P. A.,
McAlpine, C., Carleton, A. M., Hale, R., Gameda, S., Beltran-Przekurat, A.,
Baker, B., McNider, R., Legates, D. R., Shepherd, M., Du, J., Blanken, P.
D., Frauenfeld, O. W., Nair, U. S., and Fall, S.: Land cover changes and
their biogeophysical effects on climate, Int. J. Climatol., 34, 929–953,
https://doi.org/10.1002/joc.3736, 2014.
Mallard, M. S. and Spero, T. L.: Effects of mosaic land use on dynamically
downscaled WRF simulations of the contiguous United States, J. Geophys. Res.-Atmos., 124, 9117–9140, https://doi.org/10.1029/2018JD029755, 2019.
Marais, E. A. and Wiedinmyer, C.: Air Quality Impact of Diffuse and
Inefficient Combustion Emissions in Africa (DICE-Africa),
Environ. Sci. Technol., 50, 10739–10745, https://doi.org/10.1021/acs.est.6b02602, 2016.
Meng, X. H., Evans, J. P., and McCabe, M. F.: The influence of
inter-annually varying albedo on regional climate and drought, Clim. Dynam.,
42, 787–803, https://doi.org/10.1007/s00382-013-1790-0, 2014.
Merry, F., Soares-Filho, B. S., Nepstad, D., Aamacher, G., and Rodrigues, H.:
Balancing Conservation and Economic Sustainability: The Future of the Amazon
Timber Industry, Environ. Manage., 44, 395–407,
https://doi.org/10.1007/s00267-009-9337-1, 2009.
Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Sayre, R., Trabucco, A.,
and Zomer, R.: A high-resolution bioclimate map of the world: a unifying
framework for global biodiversity research and monitoring,
Global Ecol. Biogeogr., 22, 630–638, https://doi.org/10.1111/geb.12022, 2013.
Moore, N., Torbick, N., Lofgren, B., Wang, J., Pijanowski, B., Andresen, J.,
Kim, D.-Y., and Olson, J.: Adapting MODIS-derived LAI and fractional cover
into the RAMS in East Africa, Int. J. Climatol., 30, 1954–1969,
https://doi.org/10.1002/joc.2011, 2010.
Mounkaila, M. S., Abiodun, B. J., and Omotosho, J. B.: Assessing the
capability of CORDEX models in simulating onset of rainfall in West Africa,
Theor. Appl. Climatol., 119, 255–272, https://doi.org/10.1007/s00704-014-1104-4, 2015.
Mulenga, H. M.: Southern African climatic anomalies, summer rainfall and the
Angola low, Dissertation, University of Cape Town, South Africa, 1998.
Munday, C. and Washington, R.: Circulation controls on southern African
precipitation in coupled models: The role of the Angola Low, J. Geophys. Res.-Atmos., 122, 861–877, https://doi.org/10.1002/2016JD025736, 2017.
Nakanishi, M. and Niino, H.: An improved Mellor-Yamada level-3 model with
condensation physics: its design and verification, Bound.-Lay. Meteorol.,
112, 1–31, https://doi.org/10.1023/B:BOUN.0000020164.04146.98, 2004.
Nakanishi, M. and Niino, H.: An improved Mellor-Yamada level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Lay. Meteorol., 119, 397–407,
https://doi.org/10.1007/s10546-005-9030-8, 2006.
NASA/LARC/SD/ASDC: CERES Energy Balanced and Filled (EBAF) Surface Monthly means data in netCDF [Data set], NASA Langley Atmospheric Science Data Center DAAC, https://doi.org/10.5067/TERRA+AQUA/CERES/EBAF-SURFACE_L3B004.0 (last access: 16 October 2018), 2017a.
NASA/LARC/SD/ASDC: CERES Energy Balanced and Filled (EBAF) TOA Monthly means data in netCDF Edition4.0 [Data set], NASA Langley Atmospheric Science Data Center DAAC, https://doi.org/10.5067/TERRA+AQUA/CERES/EBAF-TOA_L3B004.0 (last access: 16 October 2018), 2017b.
Nepstad, D., Soares-Filho, B. S., Merry, F., Lima, A., Moutinho, P., Carter,
J., Bowman, M., Cattaneo, A., Rodrigues, H., Schwartzman, S., Mcgrath, D.,
Stickler, C., Lubowski, P. P., Rivero, S., Alencar, A., Almeida, O., and
Stella, O.: The End of Deforestation in the Brazilian Amazon, Science, 326,
1350–1351, https://doi.org/10.1126/science.1182108, 2009.
Nikulin, G., Jones, C., Giogi, F., Asrar, G., Buchner, M., Cerezo-Mota, R.,
Christensen, O. B., Deque, M., Fernandez, J., Hansler, A., van Meijgaard,
E., 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.
Niu, G.-Y. and Yang, Z.-L.: Effects of vegetation canopy processes on snow
surface energy and mass balances, J. Geophys. Res.-Atmos., 109, D23111,
https://doi.org/10.1029/2004JD004884, 2004.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res.-Atmos., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011.
Noble, E., Druyan, L. M., and Fulakeza, M.: The sensitivity of WRF daily
summertime simulations over West Africa to alternative parameterizations,
Part I: African wave circulation, Mon. Weather Rev., 142, 1588–1608,
https://doi.org/10.1175/MWR-D-13-00194.1, 2014.
Noble, E., Druyan, L. M., and Fulakeza, M.: The sensitivity of WRF daily
summertime simulations over West Africa to alternative parameterizations,
Part II: Precipitation, Mon. Weather Rev., 145, 215–233,
https://doi.org/10.1175/MWR-D-15-0294.1, 2017.
Nyamweya, C., Desjardins, C., Sigurdsson, S., Tomasson, T., Taabu-Munyaho,
A., Sitoki, L., and Stefansson, G.: Simulations of Lake Victoria circulation
patterns using the Regional Ocean Modeling System (ROMS), PLoS ONE, 11,
e0151272, https://doi.org/10.1371/journal.pone.0151272, 2016.
Odoulami, R. C., Abiodun, B. J., and Ajayi, A. E.: Modelling the potential
impacts of afforestation on extreme precipitation over West Africa, Clim. Dynam., 52, 2185–2198, https://doi.org/10.1007/s00382-018-4248-6, 2019.
Oliveira, U., Soares, B., Leitao, R. F. M., and Rodrigues, H. O.:
BioDinamica: a toolkit for analyses of biodiversity and biogeography on the
Dinamica-EGO modelling platform, Peerj, 7, e7213, https://doi.org/10.7717/Peerj.7213, 2019.
Olsen, K. W., Bonan, G. B., Levis, S., and Vertenstein, M.: Effects of land
use change on North American climate: Impact of surface datasets and model
biogeophysics, Clim. Dynam., 23, 117–132,
https://doi.org/10.1007/s00382-004-0426-9, 2004.
Otieno, V. O. and Anyah, R. O.: Effects of land use changes on climate in
the Greater Horn of Africa, Climate Res., 52, 77–95,
https://doi.org/10.3354/cr01050, 2012.
Pielke, R. A., Pitman, A., Niyogi, D., Mahmood, R., McAlpine, C., Houssain,
F., Goldewijk, K. K., Nair, U., Betts, R., and Fall, S.: Land use/land cover
changes and climate: modeling analysis and observational evidence,
WIRES Clim. Change, 2, 828–850, https://doi.org/10.1002/wcc.144, 2011.
Platnick, S.: MODIS Atmosphere L3 Monthly Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA [data set], https://doi.org/10.5067/MODIS/MOD08_M3.061 (last access: 2 October 2020), 2017.
Pleim, J. E. and Xiu, A.: Development of a land surface model, Part II: Data
assimiliation, J. Appl. Meteorol. Clim., 42, 1811–1822,
https://doi.org/10.1175/1520-0450(2003)042<1811:DOALSM>2.0.CO;2, 2003.
Pohl, B., Cretat, J., and Camberlin, P.: Testing WRF capability in
simulating the atmospheric water cycle over Equatorial East Africa, Clim. Dynam., 37, 1357–1379, https://doi.org/10.1007/s00382-011-1024-2, 2011.
Quesada, B., Arneth, A., and de Noblet-Ducoudré, N.: Atmospheric, radiative,
and hydrologic effects of land use and land cover changes: A global and
multimodel picture, J. Geophys. Res.-Atmos., 122, 5113–5131,
https://doi.org/10.1002/2016JD025448, 2017.
Ratna, S. B., Ratnam, J. V., Behera, S. K., Rautenbach, C. J. de W., Ndarana, T., Takahashi, K., and Yamagata, T.: Performance assessment of three
convective parameterization schemes in WRF for downscaling summer rainfall
over South Africa, Clim. Dynam., 42, 2931–2953,
https://doi.org/10.1007/s00382-013-1918-2, 2014.
Ratnam, J. V., Doi, T., Landman, W. A., and Behera, S. K.: Seasonal
Forecasting of Onset of Summer Rains over South Africa, J. Appl. Meteorol. Clim., 57, 2697–2711, https://doi.org/10.1175/JAMC-D-18-0067.1, 2018.
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X. W., Tsang, T.,
Strugnell, N. C., Zhang, X. Y., Jin, Y. F., Muller, J. P., Lewis, P.,
Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C.,
d'Entremont, R. P., Hu, B. X., Liang, S. L., Privette, J. L., and Roy, D.:
First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83, 135–148, https://doi.org/10.1016/S0034-4257(02)00091-3, 2002.
Schepanski, K., Knippertz, P., Fiedler, S. Timouk, F., and Demarty, J.: The
sensitivity of nocturnal low-level jets and near-surface winds over the
Sahel to model resolution, initial conditions and boundary-layer set-up, Q. J. Roy. Meteor. Soc., 141, 1442–1456, https://doi.org/10.1002/qj.2453, 2015.
Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration,
Int. J. Remote Sens., 6, 1335–1372, https://doi.org/10.1080/01431168508948283,
1985.
Silvestrini, R. A., Soares-Filho, B. S., Nepstad, D., Coe, M., Rodrigues, H.
O., and Assunção, R.: Simulating fire regimes in the Amazon in
response to climate change and deforestation, Ecol. Appl., 21, 1573–1590,
https://doi.org/10.1890/10-0827.1, 2011.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric
model for weather research and forecasting applications, J. Comput. Phys.,
227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Smirnova, T. G., Brown, J. M., Benjamin, S. G., and Kenyon, J. S.:
Modification to the Rapid Update Cycle Land Surface Model (RUC LSM)
available in the Weather Research and Forecasting (WRF) Model, Mon. Weather Rev., 144, 1851–1865, https://doi.org/10.1175/MWR-D-15-0198.1, 2016.
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database: Recent Developments and Partnerships, B. Am. Meteorol. Soc., 92, 704–708, https://doi.org/10.1175/2011BAMS3015.1, 2011.
Smith, M. C., Singarayer, J. S., Valdes, P. J., Kaplan, J. O., and Branch, N. P.: The biogeophysical climatic impacts of anthropogenic land use change during the Holocene, Clim. Past, 12, 923–941, https://doi.org/10.5194/cp-12-923-2016, 2016.
Soares-Filho, B. S., Pennachin, C. L., and Cerqueira, G.: DINAMICA – a
stochastic cellular automata model designed to simulate the landscape
dynamics in an Amazonian colonization frontier, Ecol. Model., 154, 217–235,
https://doi.org/10.1016/S0304-3800(02)00059-5, 2002.
Soares-Filho, B. S., Nepstad, D., Curran, L., Voll, E., Cerqueira, G.,
Garcia, R. A., Ramos, C. A., Mcdonald, A., Lefebvre, P., and Schlesinger, P.:
Modeling conservation in the Amazon basin, Nature, 440, 520–523,
https://doi.org/10.1038/nature04389, 2006.
Soares-Filho, B. S., Moutinho, P., Nepstad, D., Anderson, A., Rodrigues, H.,
Garcia, R., Dietzsch, L., Merry, F., Bowman, M., Hissa, L., Silvestrini, R.,
and Maretti, C.: Role of Brazilian Amazon protected areas in climate change
mitigation, P. Natl. Acad. Sci. USA, 107, 10821–10826,
https://doi.org/10.1073/pnas.0913048107, 2010.
Spera, S. A., Winter, J. M., and Chipman, J. W.: Evaluation of agricultural
land cover representations on regional climate model simulations in the
Brazilian Cerrado, J. Geophys. Res.-Atmos., 123, 5163–5176,
https://doi.org/10.1029/2017JD027989, 2018.
Subin, Z. M., Riley, W. J., Jin, J., Christianson, D. S., Torn, M. S., and
Kueppers, L. M.: Ecosystem feedbacks to climate change in California:
Development, Testing, and Analysis Using a Coupled Regional Atmosphere and
Land Surface Model (WRF3-CLM3.5), Earth Interact., 15, 1–38, https://doi.org/10.1175/2010EI331.1, 2011.
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for
climate simulations: Model structure, evaluation, and sensitivity analyses
in CESM1, J. Adv. Model. Earth Sy., 4, M02001,
https://doi.org/10.1029/2011MS000072, 2012.
Sun, S. and Xue, Y.: Implementing a new snow scheme in Simplified Simple
Biosphere Model (SSiB), Adv. Atmos. Sci., 18, 335–354,
https://doi.org/10.1007/BF02919314, 2001.
Thackeray, C. W., Flectcher, C. G., and Derksen, C.: Diagnosing the impacts
of Northern Hemisphere surface albedo on simulated climate, J. Climate, 32,
1777–1795, https://doi.org/10.1175/JCLI-D-18-0083.1, 2019.
Thapa, R. B. and Murayama, Y.: Urban growth modeling of Kathmandu
metropolitan region, Nepal, Comput. Environ. Urban, 35, 25–34,
https://doi.org/10.1016/j.compenvurbsys.2010.07.005, 2011.
Thompson, G. and Eidhammer, T.: A study of aerosol impacts on clouds and
precipitation development in a large winter cyclone, J. Atmos. Sci., 71,
3636–3658, https://doi.org/10.1175/JAS-D-13-0305.1, 2014.
Thomson, E. R., Malhi, Y., Bartholomeus, H., Oliveras, I., Gvozdevaite, A.,
Abraham, A. J., Herold, M., Adu-Bredu, S., and Doughty, C. E.: Mapping the
leaf economic spectrum across West African tropical forests using
UAV-acquired hyperspectral imagery, Remote Sens.-Basel, 10, 1532,
https://doi.org/10.3390/rs10101532, 2018.
Tian, Y., Dickinson, R. E., Zhou, L., Zeng, Z., Dai, Y., Myneni, R. B.,
Knyazikhin, Y., Zhang, Z., Friedl, M., Yu, H., Wu, W., and Shaikh, M.:
Comparison of seasonal and spatial variations of leaf area index and
fraction of absorbed photosynthetically active radiation from Moderate
Resolution Imaging Spectroradiometer (MODIS) and Common Land Model, J.
Geophys. Res.-Atmos., 109, D01103, https://doi.org/10.1029/2003JD003777, 2004a.
Tian, Y., Dickinson, R. E., Zhou, L., and Shaikh, M.: Impact of new land
boundary conditions from Moderate Resolution Imaging Spectroradiometer
(MODIS) data on the climatology of land surface variables, J. Geophys. Res.-Atmos., 109, D20115, https://doi.org/10.1029/2003JD004499, 2004b.
Towns, J., Cockerill, T., Dahan, M., Foster, I., Gaither, K., Grimshaw, A., Hazlewood, V., Lathrop, S., Lifka, D., Peterson, G. D., Roskies, R., Scott, J. R., and Wilkins-Diehr, N.: XSEDE: Accelerating Scientific Discovery, Comput. Sci. Eng., 16, 62–74, https://doi.org/10.1109/MCSE.2014.80, 2014.
Tropical Rainfall Measuring Mission (TRMM): TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/TRMM/TMPA/3H/7 (last access: 27 November 2018), 2011.
UNSD: Standard Country or Area Codes for Statistics Use, 1999 (Revision 4),
United Nations, New York, USA, available at: https://unstats.un.org/unsd/methodology/m49/ (last access: 24 May 2021), 1999.
Wang, G., Yu, M., and Xue, Y.: Modeling the potential contribution of land
cover changes to the late twentieth century Sahel drought using a regional
climate model: impact of lateral boundary conditions, Clim. Dynam., 47,
3457–3477, https://doi.org/10.1007/s00382-015-2812-x, 2016.
Wang, G., Ahmed, K. F., You, L., Yu, M., Pal, J., and Li, Z.: Projecting
regional climate and cropland changes using a linked
biogeophysical-socioeconomic modeling framework: 1. Model description and an
equilibrium application over West Africa, J. Adv. Model. Earth Sy., 9,
354–376, https://doi.org/10.1002/2016MS000712, 2017.
Wang, Z., Zeng, X., Barlage, M., Dickenson, R. E., Gao, F., and Schaaf, C.
B.: Using MODIS BRDF and albedo data to evaluate global model land surface
albedo, J. Hydrometeorol., 5, 3–14,
https://doi.org/10.1175/1525-7541(2004)005<0003:UMBAAD>2.0.CO;2, 2004.
Winckler, J., Reick, C. H., Luyssaert, S., Cescatti, A., Stoy, P. C., Lejeune, Q., Raddatz, T., Chlond, A., Heidkamp, M., and Pongratz, J.: Different response of surface temperature and air temperature to deforestation in climate models, Earth Syst. Dynam., 10, 473–484, https://doi.org/10.5194/esd-10-473-2019, 2019.
Vigaud, N., Roucou, P., Fontaine, B., Sijikumar, S., and Tyteca, S.:
WRF/APPEGE-CLIMAT simulated climate trends over West Africa, Clim. Dynam., 36, 925–944, https://doi.org/10.1007/s00382-009-0707-4, 2011.
Xia, Y., Mocko, D., Huang, M., Li, B., Rodell, M., Mitchell, K. E., Cai, X.,
and Ek, M. B.: Comparison and assessment of three advanced land surface
models in simulating terrestrial water storage components over the United
States, J. Hydrometeorol., 18, 625–649, https://doi.org/10.1175/JHM-D-16-0112.1, 2017.
Xue, T. and Shukla, J.: The influence of land surface properties on Sahel
Climate, Part 1: Desertification, J. Climate, 6, 2232–2245,
https://doi.org/10.1175/1520-0442(1993)006<2232:TIOLSP>2.0.CO;2, 1993.
Xue, Y., Sellers, P. J., Kinter, J. L., and Shukla, J.: A Simplified
Biosphere Model for Global Climate Studies, J. Climate, 4, 345–164,
https://doi.org/10.1175/1520-0442(1991)004<0345:ASBMFG>2.0.CO;2, 1991.
Xue, Y., De Sales, F., Lau, W. K.-M., Boone, A., Kim, K.-M., Mechoso, C. R.,
Wang, G., Kucharski, F., Schiro, K., Hosaka, M., Li, S., Druyan, L. M.,
Sanda, I. S., Thiaw, W., and Zeng, N.: West African monsoon decadal
variability and surface related forcing: second West African Monsoon
Modeling and Evaluation Project Experiment (WAMME II), Clim. Dynam., 47,
3517–3545, https://doi.org/10.1007/s00382-016-3224-2, 2016.
Yang, R. and Friedl, M. A.: Modeling the effects of three-dimensional
vegetation structure on surface radiation and energy balance in boreal
forests, J. Geophys. Res.-Atmos., 108, 8615, https://doi.org/10.1029/2002JD003109,
2003.
Yi, W., Gao, Z. Q., Li, Z. H., and Chen, M. S.: Land-use and land-cover sceneries in China: an application of Dinamica EGO model, in: Proc. SPIE 8513, SPIE Optical Engineering + Applications, Remote Sensing and Modeling of Ecosystems for Sustainability IX, San Diego, California, USA, 12–16 August 2012, 85130I, https://doi.org/10.1117/12.927782, 2012.
Zhang, C., Wang, Y., and Hamilton, K.: Improved representation of boundary
layer clouds over the Southeast Pacific in ARW-WRF using a modified Tiedtke
cumulus parameterization scheme, Mon. Weather Rev., 139, 3489–3513,
https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
Zhang, M., Lee, X., Yu, G., Han, S., Wang, H., Yan, J., Zhang, Y., Li, Y.,
Ohta, T., Hirano, T., Kim, J., Yoshifuji, N., and Wang, W.: Response of
surface air temperature to small-scale land clearing across latitudes,
Environ. Res. Lett., 9, 034003, https://doi.org/10.1088/1748-9326/9/3/034002, 2014.
Zhao, M. and Pitman, A. J.: The regional scale impact of land cover change
simulated with a climate model, Int. J. Climatol., 22, 271–290,
https://doi.org/10.1002/joc.727, 2002.
Zheng, Y., Kumar, A., and Niyogi, D.: Impacts of land-atmosphere coupling on
regional rainfall and convection, Clim. Dynam., 44, 2383–2409,
https://doi.org/10.1007/s00382-014-2442-8, 2015.
Short summary
Land use and land cover change is a major contributor to climate change in Africa. Here we document deficiencies in how a weather model represents the land surface of Africa and how we modify a common land surface model to overcome these deficiencies. Our tests reveal that the default weather model does not accurately predict and transition the properties of different African biomes and growing cycles. This paper demonstrates that our modified model addresses these limitations.
Land use and land cover change is a major contributor to climate change in Africa. Here we...
