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.
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
Related authors
No articles found.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Atmospheric sciences
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
The implementation of dust mineralogy in COSMO5.05-MUSCAT
Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system
Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1
Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1
Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution
Sensitivity of air quality model responses to emission changes: comparison of results based on four EU inventories through FAIRMODE benchmarking methodology
A simple and realistic aerosol emission approach for use in the Thompson–Eidhammer microphysics scheme in the NOAA UFS Weather Model (version GSL global-24Feb2022)
On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest
The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2
GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations
The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1
Spherical air mass factors in one and two dimensions with SASKTRAN 1.6.0
An improved version of the piecewise parabolic method advection scheme: description and performance assessment in a bidimensional test case with stiff chemistry in toyCTM v1.0.1
INCHEM-Py v1.2: a community box model for indoor air chemistry
Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemistry-transport model using Cloud-J v7.3e
Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations
A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest
Simulations of 7Be and 10Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 2: Influence of uncertainty factors
Advances and Prospects of Deep Learning for Medium-Range Extreme Weather Forecasting
A mountain-induced moist baroclinic wave test case for the dynamical cores of atmospheric general circulation models
The effect of emission source chemical profiles on simulated PM2.5 components: sensitivity analysis with the Community Multiscale Air Quality (CMAQ) modeling system version 5.0.2
Challenges of constructing and selecting the "perfect" initial and boundary conditions for the LES model PALM
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 1: Understanding expressiveness of schemes for different regions from the mechanism perspective
Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data
Validation and Analysis of the Polair3D v1.11 Chemical Transport Model Over Quebec
The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data
Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020
Description and evaluation of the new UM–UKCA (vn11.0) Double Extended Stratospheric–Tropospheric (DEST vn1.0) scheme for comprehensive modelling of halogen chemistry in the stratosphere
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)
Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2)
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
Short summary
Short summary
Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
Short summary
Short summary
We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
Short summary
Short summary
The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
Short summary
Short summary
We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
Short summary
Short summary
A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
Short summary
Short summary
The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
Short summary
Short summary
Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
Short summary
Short summary
Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
Short summary
Short summary
Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024, https://doi.org/10.5194/gmd-17-1111-2024, 2024
Short summary
Short summary
This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
Short summary
Short summary
With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
Short summary
Short summary
GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 17, 759–780, https://doi.org/10.5194/gmd-17-759-2024, https://doi.org/10.5194/gmd-17-759-2024, 2024
Short summary
Short summary
In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for numbers of SDs much larger than typically used in simulations.
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
Short summary
Short summary
The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 2024
Short summary
Short summary
Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
Short summary
Short summary
In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
Short summary
Short summary
We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
Short summary
Short summary
The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
Short summary
Short summary
Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
Short summary
Short summary
Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary
Short summary
A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
Short summary
Short summary
Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
Short summary
Short summary
Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
Short summary
Short summary
In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
Short summary
Short summary
This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
Short summary
Short summary
We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
Short summary
Short summary
Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
Short summary
Short summary
Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
Short summary
Short summary
Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev., 16, 7223–7235, https://doi.org/10.5194/gmd-16-7223-2023, https://doi.org/10.5194/gmd-16-7223-2023, 2023
Short summary
Short summary
The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
Short summary
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Hang, and Feijuan Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-201, https://doi.org/10.5194/gmd-2023-201, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
In this study, we have developed a model (RF-PWV) to characterize PWV variation with altitude in the study area. The RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Minjie Zheng, Hongyu Liu, Florian Adolphi, Raimund Muscheler, Zhengyao Lu, Mousong Wu, and Nønne L. Prisle
Geosci. Model Dev., 16, 7037–7057, https://doi.org/10.5194/gmd-16-7037-2023, https://doi.org/10.5194/gmd-16-7037-2023, 2023
Short summary
Short summary
The radionuclides 7Be and 10Be are useful tracers for atmospheric transport studies. Here we use the GEOS-Chem to simulate 7Be and 10Be with different production rates: the default production rate in GEOS-Chem and two from the state-of-the-art beryllium production model. We demonstrate that reduced uncertainties in the production rates can enhance the utility of 7Be and 10Be as tracers for evaluating transport and scavenging processes in global models.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6833–6856, https://doi.org/10.5194/gmd-16-6833-2023, https://doi.org/10.5194/gmd-16-6833-2023, 2023
Short summary
Short summary
In addition to the dominant role of the PBL scheme on the results of the meteorological field, many factors in the model are influenced by large uncertainties. This study focuses on the uncertainties that influence numerical simulation results (including horizontal resolution, vertical resolution, near-surface scheme, initial and boundary conditions, underlying surface update, and update of model version), hoping to provide a reference for scholars conducting research on the model.
Leonardo Olivetti and Gabriele Messori
EGUsphere, https://doi.org/10.5194/egusphere-2023-2490, https://doi.org/10.5194/egusphere-2023-2490, 2023
Short summary
Short summary
In recent years, deep learning models have emerged as a data-driven alternative to physics-based models for medium-range weather forecasting. This article provides an overview of recent developments in the field, and explores the challenges that deep learning models face when considering extreme weather events. It argues for the need to complement current approaches with models specifically designed to handle extreme events, and proposes a foundational framework to develop such models.
Owen K. Hughes and Christiane Jablonowski
Geosci. Model Dev., 16, 6805–6831, https://doi.org/10.5194/gmd-16-6805-2023, https://doi.org/10.5194/gmd-16-6805-2023, 2023
Short summary
Short summary
Atmospheric models benefit from idealized tests that assess their accuracy in a simpler simulation. A new test with artificial mountains is developed for models on a spherical earth. The mountains trigger the development of both planetary-scale and small-scale waves. These can be analyzed in dry or moist environments, with a simple rainfall mechanism. Four atmospheric models are intercompared. This sheds light on the pros and cons of the model design and the impact of mountains on the flow.
Zhongwei Luo, Yan Han, Kun Hua, Yufen Zhang, Jianhui Wu, Xiaohui Bi, Qili Dai, Baoshuang Liu, Yang Chen, Xin Long, and Yinchang Feng
Geosci. Model Dev., 16, 6757–6771, https://doi.org/10.5194/gmd-16-6757-2023, https://doi.org/10.5194/gmd-16-6757-2023, 2023
Short summary
Short summary
This study explores how the variation in the source profiles adopted in chemical transport models (CTMs) impacts the simulated results of chemical components in PM2.5 based on sensitivity analysis. The impact on PM2.5 components cannot be ignored, and its influence can be transmitted and linked between components. The representativeness and timeliness of the source profile should be paid adequate attention in air quality simulation.
Jelena Radovic, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-197, https://doi.org/10.5194/gmd-2023-197, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
The initial and boundary conditions are of crucial importance for numerical model (e.g., PALM model) validation studies and have a large influence on the model results especially in the case of studying the atmosphere of a real, complex, and densely built urban environments. Our experiments with different driving conditions for the LES model PALM show its strong dependency on them which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6635–6670, https://doi.org/10.5194/gmd-16-6635-2023, https://doi.org/10.5194/gmd-16-6635-2023, 2023
Short summary
Short summary
Most current studies on planetary boundary layer (PBL) parameterization schemes are relatively fragmented and lack systematic in-depth analysis and discussion. In this study, we comprehensively evaluate the performance capability of the PBL scheme in five typical regions of China in different seasons from the mechanism of the scheme and the effects of PBL schemes on the near-surface meteorological parameters, vertical structures of the PBL, PBL height, and turbulent diffusion.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev., 16, 6531–6552, https://doi.org/10.5194/gmd-16-6531-2023, https://doi.org/10.5194/gmd-16-6531-2023, 2023
Short summary
Short summary
It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
EGUsphere, https://doi.org/10.5194/egusphere-2023-2587, https://doi.org/10.5194/egusphere-2023-2587, 2023
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation we find that MESSy’s bias in modelling routinely observed inorganic aerosol mass concentrations is reduced. Furthermore, the representation of fine aerosol pH is particularly improved in the marine boundary layer.
Angel Liduvino Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, and Janaina P. Nascimento
Geosci. Model Dev., 16, 6413–6431, https://doi.org/10.5194/gmd-16-6413-2023, https://doi.org/10.5194/gmd-16-6413-2023, 2023
Short summary
Short summary
A 1-year simulation of atmospheric CH4 over Europe is performed and evaluated against observations based on the TROPOspheric Monitoring Instrument (TROPOMI). A good general model–observation agreement is found, with discrepancies reaching their minimum and maximum values during the summer peak season and winter months, respectively. A huge and under-explored potential for CH4 inverse modeling using improved TROPOMI XCH4 data sets in large-scale applications is identified.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Youngseob Kim, Daniel Yazgi, Andrée-Anne Brown, and Marianne Hatzopoulou
EGUsphere, https://doi.org/10.5194/egusphere-2023-2038, https://doi.org/10.5194/egusphere-2023-2038, 2023
Short summary
Short summary
Air pollution is a major health hazard, and chemical transport models are valuable tools that aid in our understanding of the risks of air pollution both at local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Zhaojun Tang, Zhe Jiang, Jiaqi Chen, Panpan Yang, and Yanan Shen
Geosci. Model Dev., 16, 6377–6392, https://doi.org/10.5194/gmd-16-6377-2023, https://doi.org/10.5194/gmd-16-6377-2023, 2023
Short summary
Short summary
We designed a new framework to facilitate emission inventory updates in the adjoint of GEOS-Chem model. It allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 meteorological data; this allows us to perform long-term analysis with consistent meteorological data.
Rui Zhu, Zhaojun Tang, Xiaokang Chen, Xiong Liu, and Zhe Jiang
Geosci. Model Dev., 16, 6337–6354, https://doi.org/10.5194/gmd-16-6337-2023, https://doi.org/10.5194/gmd-16-6337-2023, 2023
Short summary
Short summary
A single ozone (O3) tracer mode was developed in this work to build the capability of the GEOS-Chem model for rapid O3 simulation. It is combined with OMI and surface O3 observations to investigate the changes in tropospheric O3 in China in 2015–2020. The assimilations indicate rapid surface O3 increases that are underestimated by the a priori simulations. We find stronger increases in tropospheric O3 columns over polluted areas and a large discrepancy by assimilating different observations.
Ewa M. Bednarz, Ryan Hossaini, N. Luke Abraham, and Martyn P. Chipperfield
Geosci. Model Dev., 16, 6187–6209, https://doi.org/10.5194/gmd-16-6187-2023, https://doi.org/10.5194/gmd-16-6187-2023, 2023
Short summary
Short summary
Development and performance of the new DEST chemistry scheme of UM–UKCA is described. The scheme extends the standard StratTrop scheme by including important updates to the halogen chemistry, thus allowing process-oriented studies of stratospheric ozone depletion and recovery, including impacts from both controlled long-lived ozone-depleting substances and emerging issues around uncontrolled, very short-lived substances. It will thus aid studies in support of future ozone assessment reports.
Shaohui Zhou, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
Geosci. Model Dev., 16, 6247–6266, https://doi.org/10.5194/gmd-16-6247-2023, https://doi.org/10.5194/gmd-16-6247-2023, 2023
Short summary
Short summary
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indices for 13 months remain relatively stable: the forecasting accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev., 16, 6087–6125, https://doi.org/10.5194/gmd-16-6087-2023, https://doi.org/10.5194/gmd-16-6087-2023, 2023
Short summary
Short summary
We implemented an alternative aerosol scheme in the high- and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. This development enables the comparison of different aerosol schemes with different complexity in the same model framework. It identifies improvements compared to a range of observations in both the troposphere and stratosphere.
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...