Articles | Volume 13, issue 1
Geosci. Model Dev., 13, 335–362, 2020
https://doi.org/10.5194/gmd-13-335-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 335–362, 2020
https://doi.org/10.5194/gmd-13-335-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper 31 Jan 2020
Model description paper | 31 Jan 2020
An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1.0)
Naika Meili et al.
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Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-276, https://doi.org/10.5194/tc-2021-276, 2021
Preprint under review for TC
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Rain is highly variable in time at a given location, so that there can be both wet and dry climate periods. In this study, we quantified the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain-on-snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-270, https://doi.org/10.5194/gmd-2021-270, 2021
Preprint under review for GMD
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Our study introduces the SPAC-3Hpy model, a ready-to-use open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that SPAC-3Hpy can correctly simulate above- and below-ground water transport.
Stefan Fugger, Catriona L. Fyffe, Simone Fatichi, Evan Miles, Michael McCarthy, Thomas E. Shaw, Baohong Ding, Wei Yang, Patrick Wagnon, Walter Immerzeel, Qiao Liu, and Francesca Pellicciotti
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-97, https://doi.org/10.5194/tc-2021-97, 2021
Preprint under review for TC
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The monsoon is important for the shrinking and growing of glaciers in the Himalayas during summer. We calculate the melt of seven glaciers in the region using a glacier melt model and weather data. We find that monsoonal weather affects glaciers that are covered with a layer of rocky debris, and glaciers without such a layer in distinct ways. Snow cover protects the glaciers because of it's reflecting properties. This knowledge is important to predict the future of the Himalayan glaciers.
Simone Gelsinari, Valentijn R. N. Pauwels, Edoardo Daly, Jos van Dam, Remko Uijlenhoet, Nicholas Fewster-Young, and Rebecca Doble
Hydrol. Earth Syst. Sci., 25, 2261–2277, https://doi.org/10.5194/hess-25-2261-2021, https://doi.org/10.5194/hess-25-2261-2021, 2021
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Estimates of recharge to groundwater are often driven by biophysical processes occurring in the soil column and, particularly in remote areas, are also always affected by uncertainty. Using data assimilation techniques to merge remotely sensed observations with outputs of numerical models is one way to reduce this uncertainty. Here, we show the benefits of using such a technique with satellite evapotranspiration rates and coupled hydrogeological models applied to a semi-arid site in Australia.
Martina Botter, Matthias Zeeman, Paolo Burlando, and Simone Fatichi
Biogeosciences, 18, 1917–1939, https://doi.org/10.5194/bg-18-1917-2021, https://doi.org/10.5194/bg-18-1917-2021, 2021
Shovon Barua, Ian Cartwright, P. Evan Dresel, and Edoardo Daly
Hydrol. Earth Syst. Sci., 25, 89–104, https://doi.org/10.5194/hess-25-89-2021, https://doi.org/10.5194/hess-25-89-2021, 2021
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We evaluate groundwater recharge rates in a semi-arid area that has undergone land-use changes. The widespread presence of old saline groundwater indicates that pre-land-clearing recharge rates were low and present-day recharge rates are still modest. The fluctuations of the water table and tritium activities reflect present-day recharge rates; however, the water table fluctuation estimates are unrealistically high, and this technique may not be suited for estimating recharge in semi-arid areas.
Lianyu Yu, Simone Fatichi, Yijian Zeng, and Zhongbo Su
The Cryosphere, 14, 4653–4673, https://doi.org/10.5194/tc-14-4653-2020, https://doi.org/10.5194/tc-14-4653-2020, 2020
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The role of soil water and heat transfer physics in portraying the function of a cold region ecosystem was investigated. We found that explicitly considering the frozen soil physics and coupled water and heat transfer is important in mimicking soil hydrothermal dynamics. The presence of soil ice can alter the vegetation leaf onset date and deep leakage. Different complexity in representing vadose zone physics does not considerably affect interannual energy, water, and carbon fluxes.
Giulia Battista, Peter Molnar, and Paolo Burlando
Earth Surf. Dynam., 8, 619–635, https://doi.org/10.5194/esurf-8-619-2020, https://doi.org/10.5194/esurf-8-619-2020, 2020
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Suspended sediment load in rivers is highly uncertain because of spatial and temporal variability. By means of a hydrology and suspended sediment transport model, we investigated the effect of spatial variability in precipitation and surface erodibility on catchment sediment fluxes in a mesoscale river basin.
We found that sediment load depends on the spatial variability in erosion drivers, as this affects erosion rates and the location and connectivity to the channel of the erosion areas.
Nadav Peleg, Chris Skinner, Simone Fatichi, and Peter Molnar
Earth Surf. Dynam., 8, 17–36, https://doi.org/10.5194/esurf-8-17-2020, https://doi.org/10.5194/esurf-8-17-2020, 2020
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Extreme rainfall is expected to intensify with increasing temperatures, which will likely affect rainfall spatial structure. The spatial variability of rainfall can affect streamflow and sediment transport volumes and peaks. The sensitivity of the hydro-morphological response to changes in the structure of heavy rainfall was investigated. It was found that the morphological components are more sensitive to changes in rainfall spatial structure in comparison to the hydrological components.
Dietmar Dommenget, Kerry Nice, Tobias Bayr, Dieter Kasang, Christian Stassen, and Michael Rezny
Geosci. Model Dev., 12, 2155–2179, https://doi.org/10.5194/gmd-12-2155-2019, https://doi.org/10.5194/gmd-12-2155-2019, 2019
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This study describes the scientific basis for a public web page that gives access to a large set of climate model simulations. This web page is called the Monash Simple Climate Model. It provides access to more than 1300 experiments and has an interactive interface for fast analysis of the experiments and open access to the data. The study gives a short overview of the simulation experiments and discusses some of the results.
Martina Botter, Paolo Burlando, and Simone Fatichi
Hydrol. Earth Syst. Sci., 23, 1885–1904, https://doi.org/10.5194/hess-23-1885-2019, https://doi.org/10.5194/hess-23-1885-2019, 2019
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The study focuses on the solute export from rivers with the purpose of discerning the impacts of anthropic activities and catchment characteristics on water quality. The results revealed a more detectable impact of the anthropic activities than of the catchment characteristics. The solute export follows different dynamics depending on catchment characteristics and mainly on solute-specific properties. The export modality is consistent across different catchments only for a minority of solutes.
Ashley M. Broadbent, Andrew M. Coutts, Kerry A. Nice, Matthias Demuzere, E. Scott Krayenhoff, Nigel J. Tapper, and Hendrik Wouters
Geosci. Model Dev., 12, 785–803, https://doi.org/10.5194/gmd-12-785-2019, https://doi.org/10.5194/gmd-12-785-2019, 2019
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We present a simple model for assessing the cooling impacts of vegetation and water features (green and blue infrastructure) in urban environments. This model is designed to be computationally efficient so that those without technical knowledge or access to high-performance computers can use it. TARGET can be used to model average street-level air temperature at canyon to block scales (e.g. 100 m resolution). The model is carefully designed to provide reliable and accurate cooling estimates.
Dana R. Caulton, Qi Li, Elie Bou-Zeid, Jeffrey P. Fitts, Levi M. Golston, Da Pan, Jessica Lu, Haley M. Lane, Bernhard Buchholz, Xuehui Guo, James McSpiritt, Lars Wendt, and Mark A. Zondlo
Atmos. Chem. Phys., 18, 15145–15168, https://doi.org/10.5194/acp-18-15145-2018, https://doi.org/10.5194/acp-18-15145-2018, 2018
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Mobile laboratory measurements have been widely used to quantify methane emissions from point sources such as oil and gas wells, but the emission uncertainties are poorly constrained. We designed a hierarchical measurement strategy to sample natural gas emissions in the Marcellus Shale play based upon high-resolution modeling of select sites. Our study quantifies the largest sources of error with this approach and provides guidance on how to best implement mobile laboratory sampling protocols.
Enrica Perra, Monica Piras, Roberto Deidda, Claudio Paniconi, Giuseppe Mascaro, Enrique R. Vivoni, Pierluigi Cau, Pier Andrea Marras, Ralf Ludwig, and Swen Meyer
Hydrol. Earth Syst. Sci., 22, 4125–4143, https://doi.org/10.5194/hess-22-4125-2018, https://doi.org/10.5194/hess-22-4125-2018, 2018
Dusan Jovanovic, Tijana Jovanovic, Alfonso Mejía, Jon Hathaway, and Edoardo Daly
Hydrol. Earth Syst. Sci., 22, 3551–3559, https://doi.org/10.5194/hess-22-3551-2018, https://doi.org/10.5194/hess-22-3551-2018, 2018
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A relationship between the Hurst (H) exponent (a long-term correlation coefficient) within a flow time series and various catchment characteristics for a number of catchments in the USA and Australia was investigated. A negative relationship with imperviousness was identified, which allowed for an efficient catchment classification, thus making the H exponent a useful metric to quantitatively assess the impact of catchment imperviousness on streamflow regime.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
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Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Sahani Pathiraja, Daniela Anghileri, Paolo Burlando, Ashish Sharma, Lucy Marshall, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 22, 2903–2919, https://doi.org/10.5194/hess-22-2903-2018, https://doi.org/10.5194/hess-22-2903-2018, 2018
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Hydrologic modeling methodologies must be developed that are capable of predicting runoff in catchments with changing land cover conditions. This article investigates the efficacy of a recently developed approach that allows for runoff prediction in catchments with unknown land cover changes, through experimentation in a deforested catchment in Vietnam. The importance of key elements of the method in ensuring its success, such as the chosen hydrologic model, is investigated.
Katrina E. Bennett, Theodore J. Bohn, Kurt Solander, Nathan G. McDowell, Chonggang Xu, Enrique Vivoni, and Richard S. Middleton
Hydrol. Earth Syst. Sci., 22, 709–725, https://doi.org/10.5194/hess-22-709-2018, https://doi.org/10.5194/hess-22-709-2018, 2018
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We applied the Variable Infiltration Capacity hydrologic model to examine scenarios of change under climate and landscape disturbances in the San Juan River basin, a major sub-watershed of the Colorado River basin. Climate change coupled with landscape disturbance leads to reduced streamflow in the San Juan River basin. Disturbances are expected to be widespread in this region. Therefore, accounting for these changes within the context of climate change is imperative for water resource planning.
Nadav Peleg, Frank Blumensaat, Peter Molnar, Simone Fatichi, and Paolo Burlando
Hydrol. Earth Syst. Sci., 21, 1559–1572, https://doi.org/10.5194/hess-21-1559-2017, https://doi.org/10.5194/hess-21-1559-2017, 2017
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We investigated the relative contribution of the spatial versus climatic rainfall variability for flow peaks by applying an advanced stochastic rainfall generator to simulate rainfall for a small urban catchment and simulate flow dynamics in the sewer system. We found that the main contribution to the total flow variability originates from the natural climate variability. The contribution of spatial rainfall variability to the total flow variability was found to increase with return periods.
Caitlin E. Moore, Jason Beringer, Bradley Evans, Lindsay B. Hutley, and Nigel J. Tapper
Biogeosciences, 14, 111–129, https://doi.org/10.5194/bg-14-111-2017, https://doi.org/10.5194/bg-14-111-2017, 2017
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Separating tree and grass productivity dynamics in savanna ecosystems is vital for understanding how they function over time. We showed how tree-grass phenology information can improve model estimates of gross primary productivity in an Australian tropical savanna. Our findings will contribute towards improved modelling of productivity in savannas, which will assist with their management into the future.
Valentijn R. N. Pauwels and Edoardo Daly
Hydrol. Earth Syst. Sci., 20, 4689–4706, https://doi.org/10.5194/hess-20-4689-2016, https://doi.org/10.5194/hess-20-4689-2016, 2016
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We demonstrate that the classical approach to solve the surface energy balance equation in land surface models has its issues, and we propose an improved method.
Jason Beringer, Lindsay B. Hutley, Ian McHugh, Stefan K. Arndt, David Campbell, Helen A. Cleugh, James Cleverly, Víctor Resco de Dios, Derek Eamus, Bradley Evans, Cacilia Ewenz, Peter Grace, Anne Griebel, Vanessa Haverd, Nina Hinko-Najera, Alfredo Huete, Peter Isaac, Kasturi Kanniah, Ray Leuning, Michael J. Liddell, Craig Macfarlane, Wayne Meyer, Caitlin Moore, Elise Pendall, Alison Phillips, Rebecca L. Phillips, Suzanne M. Prober, Natalia Restrepo-Coupe, Susanna Rutledge, Ivan Schroder, Richard Silberstein, Patricia Southall, Mei Sun Yee, Nigel J. Tapper, Eva van Gorsel, Camilla Vote, Jeff Walker, and Tim Wardlaw
Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, https://doi.org/10.5194/bg-13-5895-2016, 2016
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OzFlux is the regional Australian and New Zealand flux tower network that aims to provide a continental-scale national facility to monitor and assess trends, and improve predictions, of Australia’s terrestrial biosphere and climate. We describe the evolution, design, and status as well as an overview of data processing. We suggest that a synergistic approach is required to address all of the spatial, ecological, human, and cultural challenges of managing Australian ecosystems.
Bahareh Kianfar, Simone Fatichi, Athansios Paschalis, Max Maurer, and Peter Molnar
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-536, https://doi.org/10.5194/hess-2016-536, 2016
Revised manuscript has not been submitted
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Raingauge observations show a large variability in extreme rainfall depths in the current climate. Climate model predictions of extreme rainfall in the future have to be compared with this natural variability. Our work shows that predictions of future extreme rainfall often lie within the range of natural variability of present-day climate, and therefore predictions of change are highly uncertain. We demonstrate this by using stochastic rainfall models and 10-min rainfall data in Switzerland.
Caitlin E. Moore, Jason Beringer, Bradley Evans, Lindsay B. Hutley, Ian McHugh, and Nigel J. Tapper
Biogeosciences, 13, 2387–2403, https://doi.org/10.5194/bg-13-2387-2016, https://doi.org/10.5194/bg-13-2387-2016, 2016
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Savannas cover 20 % of the global land surface and account for 25 % of global terrestrial carbon uptake. They support 20 % of the world’s human population and are one of the most important ecosystems on our planet. We evaluated the temporal partitioning of carbon between overstory and understory in Australian tropical savanna using eddy covariance. We found the understory contributed ~ 32 % to annual productivity, increasing to 40 % in the wet season, thus driving seasonality in carbon uptake.
A. P. Schreiner-McGraw, E. R. Vivoni, G. Mascaro, and T. E. Franz
Hydrol. Earth Syst. Sci., 20, 329–345, https://doi.org/10.5194/hess-20-329-2016, https://doi.org/10.5194/hess-20-329-2016, 2016
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Soil moisture estimates from a novel method were evaluated in two semiarid watersheds. We found good agreements between the technique and estimates derived from watershed instruments designed to close the water balance. We then investigated local hydrologic processes and link between evapotranspiration and soil moisture obtained from the novel measurements.
P. Molnar, S. Fatichi, L. Gaál, J. Szolgay, and P. Burlando
Hydrol. Earth Syst. Sci., 19, 1753–1766, https://doi.org/10.5194/hess-19-1753-2015, https://doi.org/10.5194/hess-19-1753-2015, 2015
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We present an empirical study of the rates of increase in precipitation intensity with air temperature using high-resolution 10 min precipitation records in Switzerland. We estimated the scaling rates for lightning (convective) and non-lightning event subsets and show that scaling rates are between 7 and 14%/C for convective rain and that mixing of storm types exaggerates the relations to air temperature. Doubled CC rates reported by other studies are an exception in our data set.
M. Piras, G. Mascaro, R. Deidda, and E. R. Vivoni
Hydrol. Earth Syst. Sci., 18, 5201–5217, https://doi.org/10.5194/hess-18-5201-2014, https://doi.org/10.5194/hess-18-5201-2014, 2014
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We quantified the hydrologic impacts of climate change in the Rio Mannu basin (472.5 km2), Sardinia, Italy.
We created high-resolution climate forcings for a physically based distributed hydrologic model by combining four climate models with two statistical downscaling tools of precipitation and potential evapotranspiration. A significant diminution of mean annual runoff at the basin outlet (mean of -32%), and a reduction of soil water content and actual evapotranspiration are expected.
G. Mascaro, M. Piras, R. Deidda, and E. R. Vivoni
Hydrol. Earth Syst. Sci., 17, 4143–4158, https://doi.org/10.5194/hess-17-4143-2013, https://doi.org/10.5194/hess-17-4143-2013, 2013
E. Velasco, M. Roth, S. H. Tan, M. Quak, S. D. A. Nabarro, and L. Norford
Atmos. Chem. Phys., 13, 10185–10202, https://doi.org/10.5194/acp-13-10185-2013, https://doi.org/10.5194/acp-13-10185-2013, 2013
T. Grünewald, J. Stötter, J. W. Pomeroy, R. Dadic, I. Moreno Baños, J. Marturià, M. Spross, C. Hopkinson, P. Burlando, and M. Lehning
Hydrol. Earth Syst. Sci., 17, 3005–3021, https://doi.org/10.5194/hess-17-3005-2013, https://doi.org/10.5194/hess-17-3005-2013, 2013
S. Fatichi, S. Rimkus, P. Burlando, R. Bordoy, and P. Molnar
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-3743-2013, https://doi.org/10.5194/hessd-10-3743-2013, 2013
Revised manuscript not accepted
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Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
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This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Paolo Pelucchi, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 14, 5413–5434, https://doi.org/10.5194/gmd-14-5413-2021, https://doi.org/10.5194/gmd-14-5413-2021, 2021
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Stratocumulus are thin clouds whose cloud cover is underestimated in climate models partly due to overly low vertical resolution. We develop a scheme that locally refines the vertical grid based on a physical constraint for the cloud top. Global simulations show that the scheme, implemented only in the radiation routine, can increase stratocumulus cloud cover. However, this effect is poorly propagated to the simulated cloud cover. The scheme's limitations and possible ways forward are discussed.
John G. Virgin, Christopher G. Fletcher, Jason N. S. Cole, Knut von Salzen, and Toni Mitovski
Geosci. Model Dev., 14, 5355–5372, https://doi.org/10.5194/gmd-14-5355-2021, https://doi.org/10.5194/gmd-14-5355-2021, 2021
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Equilibrium climate sensitivity, or the amount of warming the Earth would exhibit a result of a doubling of atmospheric CO2, is a common metric used in assessments of climate models. Here, we compare climate sensitivity between two versions of the Canadian Earth System Model. We find the newest iteration of the model (version 5) to have higher climate sensitivity due to reductions in low-level clouds, which reflect radiation and cool the planet, as the surface warms.
Matthias Mengel, Simon Treu, Stefan Lange, and Katja Frieler
Geosci. Model Dev., 14, 5269–5284, https://doi.org/10.5194/gmd-14-5269-2021, https://doi.org/10.5194/gmd-14-5269-2021, 2021
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To identify the impacts of historical climate change it is necessary to separate the effect of the different impact drivers. To address this, one needs to compare historical impacts to a counterfactual world with impacts that would have been without climate change. We here present an approach that produces counterfactual climate data and can be used in climate impact models to simulate counterfactual impacts. We make these data available through the ISIMIP project.
Yixiong Lu, Tongwen Wu, Yubin Li, and Ben Yang
Geosci. Model Dev., 14, 5183–5204, https://doi.org/10.5194/gmd-14-5183-2021, https://doi.org/10.5194/gmd-14-5183-2021, 2021
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The spurious precipitation in the tropical southeastern Pacific and southern Atlantic is one of the most prominent systematic biases in coupled atmosphere–ocean general circulation models. This study significantly promotes the marine stratus simulation and largely alleviates the excessive precipitation biases through improving parameterizations of boundary-layer turbulence and shallow convection, providing an effective solution to the long-standing bias in the tropical precipitation simulation.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
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We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Paul A. Ullrich, Colin M. Zarzycki, Elizabeth E. McClenny, Marielle C. Pinheiro, Alyssa M. Stansfield, and Kevin A. Reed
Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, https://doi.org/10.5194/gmd-14-5023-2021, 2021
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TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets. Version 2.1 of TE now provides extensive support for nodal and areal features. This paper describes the algorithms that have been added to the TE framework since version 1.0 and gives several examples of how these can be combined to produce composite algorithms for evaluating and understanding atmospheric features.
Tobias Peter Bauer, Peter Holtermann, Bernd Heinold, Hagen Radtke, Oswald Knoth, and Knut Klingbeil
Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, https://doi.org/10.5194/gmd-14-4843-2021, 2021
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We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of using the latest coupling technologies are discussed in detail. An exchange grid handles the different coastlines from the unstructured atmosphere and the structured ocean grids. Due to a high level of automated processing, ICONGETM requires only minimal user input. The application to a coastal upwelling scenario demonstrates significantly improved model results compared to uncoupled simulations.
Dawn L. Woodard, Alexey N. Shiklomanov, Ben Kravitz, Corinne Hartin, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 4751–4767, https://doi.org/10.5194/gmd-14-4751-2021, https://doi.org/10.5194/gmd-14-4751-2021, 2021
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We have added a representation of the permafrost carbon feedback to the simple, open-source global carbon–climate model Hector and calibrated the results to be consistent with historical data and Earth system model projections. Our results closely match previous work, estimating around 0.2 °C of warming from permafrost this century. This capability will be useful to explore uncertainties in this feedback and for coupling with integrated assessment models for policy and economic analysis.
Klaus Wyser, Torben Koenigk, Uwe Fladrich, Ramon Fuentes-Franco, Mehdi Pasha Karami, and Tim Kruschke
Geosci. Model Dev., 14, 4781–4796, https://doi.org/10.5194/gmd-14-4781-2021, https://doi.org/10.5194/gmd-14-4781-2021, 2021
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This paper describes the large ensemble done by SMHI with the EC-Earth3 climate model. The ensemble comprises 50 realizations for each of the historical experiments after 1970 and four different future projections for CMIP6. We describe the creation of the initial states for the ensemble and the reduced set of output variables. A first look at the results illustrates the changes in the climate during this century and puts them in relation to the uncertainty from the model's internal variability.
Yidi Xu, Philippe Ciais, Le Yu, Wei Li, Xiuzhi Chen, Haicheng Zhang, Chao Yue, Kasturi Kanniah, Arthur P. Cracknell, and Peng Gong
Geosci. Model Dev., 14, 4573–4592, https://doi.org/10.5194/gmd-14-4573-2021, https://doi.org/10.5194/gmd-14-4573-2021, 2021
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In this study, we implemented the specific morphology, phenology and harvest process of oil palm in the global land surface model ORCHIDEE-MICT. The improved model generally reproduces the same leaf area index, biomass density and life cycle fruit yield as observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.
Benjamin A. Toms, Karthik Kashinath, Prabhat, and Da Yang
Geosci. Model Dev., 14, 4495–4508, https://doi.org/10.5194/gmd-14-4495-2021, https://doi.org/10.5194/gmd-14-4495-2021, 2021
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We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 14, 4429–4441, https://doi.org/10.5194/gmd-14-4429-2021, https://doi.org/10.5194/gmd-14-4429-2021, 2021
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Soil moisture is of great importance for weather and climate. We present a machine learning model that produces accurate predictions of satellite-observed surface soil moisture, based on meteorological data from a climate model. It can be used as soil moisture parametrisation in climate models and to produce comprehensive global soil moisture datasets. Moreover, it may motivate similar applications of machine learning in climate science.
Jeremy McGibbon, Noah D. Brenowitz, Mark Cheeseman, Spencer K. Clark, Johann P. S. Dahm, Eddie C. Davis, Oliver D. Elbert, Rhea C. George, Lucas M. Harris, Brian Henn, Anna Kwa, W. Andre Perkins, Oliver Watt-Meyer, Tobias F. Wicky, Christopher S. Bretherton, and Oliver Fuhrer
Geosci. Model Dev., 14, 4401–4409, https://doi.org/10.5194/gmd-14-4401-2021, https://doi.org/10.5194/gmd-14-4401-2021, 2021
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FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run fast, but this makes it hard to add features if you do not (or even if you do) know Fortran. We have written a Python interface to FV3GFS that lets you import the Fortran model as a Python package. We show examples of how this is used to write
modelscripts, which reproduce or build on what the Fortran model can do. You could do this same wrapping for any compiled model, not just FV3GFS.
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev., 14, 4335–4355, https://doi.org/10.5194/gmd-14-4335-2021, https://doi.org/10.5194/gmd-14-4335-2021, 2021
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Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Daniel J. Lunt, Deepak Chandan, Alan M. Haywood, George M. Lunt, Jonathan C. Rougier, Ulrich Salzmann, Gavin A. Schmidt, and Paul J. Valdes
Geosci. Model Dev., 14, 4307–4317, https://doi.org/10.5194/gmd-14-4307-2021, https://doi.org/10.5194/gmd-14-4307-2021, 2021
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Often in science we carry out experiments with computers in which several factors are explored, for example, in the field of climate science, how the factors of greenhouse gases, ice, and vegetation affect temperature. We can explore the relative importance of these factors by
swapping in and outdifferent values of these factors, and can also carry out experiments with many different combinations of these factors. This paper discusses how best to analyse the results from such experiments.
Markus Adloff, Andy Ridgwell, Fanny M. Monteiro, Ian J. Parkinson, Alexander J. Dickson, Philip A. E. Pogge von Strandmann, Matthew S. Fantle, and Sarah E. Greene
Geosci. Model Dev., 14, 4187–4223, https://doi.org/10.5194/gmd-14-4187-2021, https://doi.org/10.5194/gmd-14-4187-2021, 2021
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We present the first representation of the trace metals Sr, Os, Li and Ca in a 3D Earth system model (cGENIE). The simulation of marine metal sources (weathering, hydrothermal input) and sinks (deposition) reproduces the observed concentrations and isotopic homogeneity of these metals in the modern ocean. With these new tracers, cGENIE can be used to test hypotheses linking these metal cycles and the cycling of other elements like O and C and simulate their dynamic response to external forcing.
Alessandro Anav, Adriana Carillo, Massimiliano Palma, Maria Vittoria Struglia, Ufuk Utku Turuncoglu, and Gianmaria Sannino
Geosci. Model Dev., 14, 4159–4185, https://doi.org/10.5194/gmd-14-4159-2021, https://doi.org/10.5194/gmd-14-4159-2021, 2021
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The Mediterranean Basin is a complex region, characterized by the presence of pronounced topography and a complex land–sea distribution including a considerable number of islands and straits; these features generate strong local atmosphere–sea interactions.
Regional Earth system models have been developed and used to study both present and future Mediterranean climate systems. The main aims of this paper are to present and evaluate the newly developed regional Earth system model ENEA-REG.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
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In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Dirk Barbi, Nadine Wieters, Paul Gierz, Miguel Andrés-Martínez, Deniz Ural, Fatemeh Chegini, Sara Khosravi, and Luisa Cristini
Geosci. Model Dev., 14, 4051–4067, https://doi.org/10.5194/gmd-14-4051-2021, https://doi.org/10.5194/gmd-14-4051-2021, 2021
Cléa Denamiel, Petra Pranić, Damir Ivanković, Iva Tojčić, and Ivica Vilibić
Geosci. Model Dev., 14, 3995–4017, https://doi.org/10.5194/gmd-14-3995-2021, https://doi.org/10.5194/gmd-14-3995-2021, 2021
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The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017) are evaluated against available observational datasets in the Adriatic region. Generally, the AdriSC model performs better than regional climate models that have resolutions that are 4 times more coarse, except concerning summer temperatures, which are systematically underestimated. High-resolution climate models may thus provide new insights about the local impacts of global warming in the Adriatic.
Petra Pranić, Cléa Denamiel, and Ivica Vilibić
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-155, https://doi.org/10.5194/gmd-2021-155, 2021
Revised manuscript accepted for GMD
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The Adriatic Sea and Coast model was developed due to the need for higher-resolution climate models and longer-term simulations to capture the coastal atmospheric and ocean processes at climate scales in the Adriatic Sea. The ocean results of a 31-year long simulation were compared to the observational data. The evaluation revealed that the model is capable to reproduce the observed physical properties with good accuracy and can be further used to study the dynamics of the Adriatic-Ionian basin.
Guy Munhoven
Geosci. Model Dev., 14, 3603–3631, https://doi.org/10.5194/gmd-14-3603-2021, https://doi.org/10.5194/gmd-14-3603-2021, 2021
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Sea-floor sediments play an important role in biogeochemical cycling of elements (e.g. carbon, silicon, nutrients) in the ocean. Realistic sediment modules are, however, not yet commonly used in global ocean biogeochemical models. Here we present MEDUSA, a model of the processes taking place in the surface sea-floor sediments which control the interaction between the sediments and the ocean. MEDUSA can be configured to meet the exact needs of any given ocean biogeochemical model.
Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551, https://doi.org/10.5194/gmd-14-3539-2021, https://doi.org/10.5194/gmd-14-3539-2021, 2021
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We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
Jens Pfafferott, Sascha Rißmann, Matthias Sühring, Farah Kanani-Sühring, and Björn Maronga
Geosci. Model Dev., 14, 3511–3519, https://doi.org/10.5194/gmd-14-3511-2021, https://doi.org/10.5194/gmd-14-3511-2021, 2021
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The building model is integrated via an urban surface model into the urban climate model.
There is a strong interaction between the built environment and the urban climate.
According to the building energy concept, the energy demand results in a waste heat; this is directly transferred to the urban environment.
The impact of buildings on the urban climate is defined by different physical building parameters with different technical facilities for ventilation, heating and cooling.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
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This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Jun'ya Takakura, Shinichiro Fujimori, Kiyoshi Takahashi, Naota Hanasaki, Tomoko Hasegawa, Yukiko Hirabayashi, Yasushi Honda, Toshichika Iizumi, Chan Park, Makoto Tamura, and Yasuaki Hijioka
Geosci. Model Dev., 14, 3121–3140, https://doi.org/10.5194/gmd-14-3121-2021, https://doi.org/10.5194/gmd-14-3121-2021, 2021
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To simplify calculating economic impacts of climate change, statistical methods called emulators are developed and evaluated. There are trade-offs between model complexity and emulation performance. Aggregated economic impacts can be approximated by relatively simple emulators, but complex emulators are necessary to accommodate finer-scale economic impacts.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094, https://doi.org/10.5194/gmd-14-3079-2021, https://doi.org/10.5194/gmd-14-3079-2021, 2021
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The Multivariable Integrated Evaluation Tool (MVIETool) is a simple-to-use and straightforward tool designed for evaluation and intercomparison of climate models in terms of vector fields or multiple fields. The tool incorporates some new improvements in vector field evaluation (VFE) and multivariable integrated evaluation (MVIE) methods, which are introduced in this paper.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
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The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Nicholas J. Leach, Stuart Jenkins, Zebedee Nicholls, Christopher J. Smith, John Lynch, Michelle Cain, Tristram Walsh, Bill Wu, Junichi Tsutsui, and Myles R. Allen
Geosci. Model Dev., 14, 3007–3036, https://doi.org/10.5194/gmd-14-3007-2021, https://doi.org/10.5194/gmd-14-3007-2021, 2021
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This paper presents an update of the FaIR simple climate model, which can estimate the impact of anthropogenic greenhouse gas and aerosol emissions on the global climate. This update aims to significantly increase the structural simplicity of the model, making it more understandable and transparent. This simplicity allows it to be implemented in a wide range of environments, including Excel. We suggest that it could be used widely in academia, corporate research, and education.
Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu, and Aixue Hu
Geosci. Model Dev., 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021, https://doi.org/10.5194/gmd-14-2977-2021, 2021
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This paper presents the high-resolution version of the Beijing Climate Center (BCC) Climate System Model, BCC-CSM2-HR, and describes its climate simulation performance including the atmospheric temperature and wind; precipitation; and the tropical climate phenomena such as TC, MJO, QBO, and ENSO. BCC-CSM2-HR is our model version contributing to the HighResMIP. We focused on its updates and differential characteristics from its predecessor, the medium-resolution version BCC-CSM2-MR.
Olivier Marti, Sébastien Nguyen, Pascale Braconnot, Sophie Valcke, Florian Lemarié, and Eric Blayo
Geosci. Model Dev., 14, 2959–2975, https://doi.org/10.5194/gmd-14-2959-2021, https://doi.org/10.5194/gmd-14-2959-2021, 2021
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State-of-the-art Earth system models, like the ones used in CMIP6, suffer from temporal inconsistencies at the ocean–atmosphere interface. In this study, a mathematically consistent iterative Schwarz method is used as a reference. Its tremendous computational cost makes it unusable for production runs, but it allows us to evaluate the error made when using legacy coupling schemes. The impact on the climate at longer timescales of days to decades is not evaluated.
Lynsay Spafford and Andrew Hugh MacDougall
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-150, https://doi.org/10.5194/gmd-2021-150, 2021
Revised manuscript accepted for GMD
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Land biogeochemical cycles influence global climate change. Their influence is examined through complex computer models which account for the interaction of the land, ocean, and atmosphere. Improved models used in the recent round of model intercomparison used inconsistent validation methods to compare simulated land biogeochemistry to datasets. For the next round of model intercomparison we recommend a validation protocol with explicit reference datasets and informative performance metrics.
Steven R. Brus, Phillip J. Wolfram, Luke P. Van Roekel, and Jessica D. Meixner
Geosci. Model Dev., 14, 2917–2938, https://doi.org/10.5194/gmd-14-2917-2021, https://doi.org/10.5194/gmd-14-2917-2021, 2021
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Wind-generated waves are an important process in the global climate system. They mediate many interactions between the ocean, atmosphere, and sea ice. Models which describe these waves are computationally expensive and have often been excluded from coupled Earth system models. To address this, we have developed a capability for the WAVEWATCH III model which allows model resolution to be varied globally across the coastal open ocean. This allows for improved accuracy at reduced computing time.
Elisa Ziegler and Kira Rehfeld
Geosci. Model Dev., 14, 2843–2866, https://doi.org/10.5194/gmd-14-2843-2021, https://doi.org/10.5194/gmd-14-2843-2021, 2021
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Past climate changes are the only record of how the climate responds to changes in conditions on Earth, but simulations with complex climate models are challenging. We extended a simple climate model such that it simulates the development of temperatures over time. In the model, changes in carbon dioxide and ice distribution affect the simulated temperatures the most. The model is very efficient and can therefore be used to examine past climate changes happening over long periods of time.
Qun Liu, Matthew Collins, Penelope Maher, Stephen I. Thomson, and Geoffrey K. Vallis
Geosci. Model Dev., 14, 2801–2826, https://doi.org/10.5194/gmd-14-2801-2021, https://doi.org/10.5194/gmd-14-2801-2021, 2021
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Clouds play an vital role in Earth's energy budget, and even a small change in cloud fields can have a large impact on the climate system. They also bring lots of uncertainties to climate models. Here we implement a simple diagnostic cloud scheme in order to reproduce the general radiative properties of clouds. The scheme can capture some key features of the cloud fraction and cloud radiative properties and thus provide a useful tool to explore unsolved problems relating to clouds.
Martina Messmer, Santos J. González-Rojí, Christoph C. Raible, and Thomas F. Stocker
Geosci. Model Dev., 14, 2691–2711, https://doi.org/10.5194/gmd-14-2691-2021, https://doi.org/10.5194/gmd-14-2691-2021, 2021
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Sensitivity experiments with the WRF model are run to find an optimal parameterization setup for precipitation around Mount Kenya at a scale that resolves convection (1 km). Precipitation is compared against many weather stations and gridded observational data sets. Both the temporal correlation of precipitation sums and pattern correlations show that fewer nests lead to a more constrained simulation with higher correlation. The Grell–Freitas cumulus scheme obtains the most accurate results.
Pengfei Wang, Jinrong Jiang, Pengfei Lin, Mengrong Ding, Junlin Wei, Feng Zhang, Lian Zhao, Yiwen Li, Zipeng Yu, Weipeng Zheng, Yongqiang Yu, Xuebin Chi, and Hailong Liu
Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, https://doi.org/10.5194/gmd-14-2781-2021, 2021
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Global ocean general circulation models are a fundamental tool for oceanography research, ocean forecast, and climate change research. The increasing resolution will greatly improve simulations of the models, but it also demands much more computing resources. In this study, we have ported an ocean general circulation model to a heterogeneous computing system and have developed a 3–5 km model version. A 14-year integration has been conducted and the preliminary results have been evaluated.
Chao Sun, Li Liu, Ruizhe Li, Xinzhu Yu, Hao Yu, Biao Zhao, Guansuo Wang, Juanjuan Liu, Fangli Qiao, and Bin Wang
Geosci. Model Dev., 14, 2635–2657, https://doi.org/10.5194/gmd-14-2635-2021, https://doi.org/10.5194/gmd-14-2635-2021, 2021
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Data assimilation (DA) provides better initial states of model runs by combining observations and models. This work focuses on the technical challenges in developing a coupled ensemble-based DA system and proposes a new DA framework DAFCC1 based on C-Coupler2. DAFCC1 enables users to conveniently integrate a DA method into a model with automatic and efficient data exchanges. A sample DA system that combines GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.
Andrew J. Wiltshire, Eleanor J. Burke, Sarah E. Chadburn, Chris D. Jones, Peter M. Cox, Taraka Davies-Barnard, Pierre Friedlingstein, Anna B. Harper, Spencer Liddicoat, Stephen Sitch, and Sönke Zaehle
Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, https://doi.org/10.5194/gmd-14-2161-2021, 2021
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Limited nitrogen availbility can restrict the growth of plants and their ability to assimilate carbon. It is important to include the impact of this process on the global land carbon cycle. This paper presents a model of the coupled land carbon and nitrogen cycle, which is included within the UK Earth System model to improve projections of climate change and impacts on ecosystems.
Pirkka Ollinaho, Glenn D. Carver, Simon T. K. Lang, Lauri Tuppi, Madeleine Ekblom, and Heikki Järvinen
Geosci. Model Dev., 14, 2143–2160, https://doi.org/10.5194/gmd-14-2143-2021, https://doi.org/10.5194/gmd-14-2143-2021, 2021
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OpenEnsemble 1.0 is a novel dataset that aims to open ensemble or probabilistic weather forecasting research up to the academic community. The dataset contains atmospheric states that are required for running model forecasts of atmospheric evolution. Our capacity to observe the atmosphere is limited; thus, a single reconstruction of the atmospheric state contains some errors. Our dataset provides sets of 50 slightly different atmospheric states so that these errors can be taken into account.
Yan Sun, Daniel S. Goll, Jinfeng Chang, Philippe Ciais, Betrand Guenet, Julian Helfenstein, Yuanyuan Huang, Ronny Lauerwald, Fabienne Maignan, Victoria Naipal, Yilong Wang, Hui Yang, and Haicheng Zhang
Geosci. Model Dev., 14, 1987–2010, https://doi.org/10.5194/gmd-14-1987-2021, https://doi.org/10.5194/gmd-14-1987-2021, 2021
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We evaluated the performance of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 against remote sensing, ground-based measurement networks and ecological databases. The simulated carbon, nitrogen and phosphorus fluxes among different spatial scales are generally in good agreement with data-driven estimates. However, the recent carbon sink in the Northern Hemisphere is substantially underestimated. Potential causes and model development priorities are discussed.
Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, and Huiping Yan
Geosci. Model Dev., 14, 1921–1948, https://doi.org/10.5194/gmd-14-1921-2021, https://doi.org/10.5194/gmd-14-1921-2021, 2021
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Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.
Johannes Horak, Marlis Hofer, Ethan Gutmann, Alexander Gohm, and Mathias W. Rotach
Geosci. Model Dev., 14, 1657–1680, https://doi.org/10.5194/gmd-14-1657-2021, https://doi.org/10.5194/gmd-14-1657-2021, 2021
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This process-based evaluation of the atmospheric model ICAR is conducted to derive recommendations to increase the likelihood of its results being correct for the right reasons. We conclude that a different diagnosis of the atmospheric background state is necessary, as well as a model top at an elevation of at least 10 km. Alternative boundary conditions at the top were not found to be effective in reducing this model top elevation. The results have wide implications for future ICAR studies.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
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A stochastic deep convection parameterization is implemented into the US Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of
too much light rain and too little heavy rainis largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in the deep convective parameterization.
Sonia Jerez, Laura Palacios-Peña, Claudia Gutiérrez, Pedro Jiménez-Guerrero, Jose María López-Romero, Enrique Pravia-Sarabia, and Juan Pedro Montávez
Geosci. Model Dev., 14, 1533–1551, https://doi.org/10.5194/gmd-14-1533-2021, https://doi.org/10.5194/gmd-14-1533-2021, 2021
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This research explores the role of aerosols when modeling surface solar radiation at regional scales (over Europe). A set of model experiments was performed with and without dynamical modeling of atmospheric aerosols and their direct and indirect effects on radiation. Results showed significant differences in the simulated solar radiation, mainly driven by the aerosol impact on cloudiness, which calls for caution when interpreting model experiments that do not include aerosols.
Anna Vaughan, Will Tebbutt, J. Scott Hosking, and Richard E. Turner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-420, https://doi.org/10.5194/gmd-2020-420, 2021
Revised manuscript accepted for GMD
Sara Top, Lola Kotova, Lesley De Cruz, Svetlana Aniskevich, Leonid Bobylev, Rozemien De Troch, Natalia Gnatiuk, Anne Gobin, Rafiq Hamdi, Arne Kriegsmann, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, Viesturs Zandersons, Philippe De Maeyer, Piet Termonia, and Steven Caluwaerts
Geosci. Model Dev., 14, 1267–1293, https://doi.org/10.5194/gmd-14-1267-2021, https://doi.org/10.5194/gmd-14-1267-2021, 2021
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Detailed climate data are needed to assess the impact of climate change on human and natural systems. The performance of two high-resolution regional climate models, ALARO-0 and REMO2015, was investigated over central Asia, a vulnerable region where detailed climate information is scarce. In certain subregions the produced climate data are suitable for impact studies, but bias adjustment is required for subregions where significant biases have been identified.
Yaqiong Lu and Xianyu Yang
Geosci. Model Dev., 14, 1253–1265, https://doi.org/10.5194/gmd-14-1253-2021, https://doi.org/10.5194/gmd-14-1253-2021, 2021
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Crop growth in land surface models normally requires high-temporal-resolution climate data, but such high-temporal-resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choices if there is an interest in a particular climate simulation that only saved monthly outputs. Our work provides an alternative way to use the monthly climate for crop yield projections. Such an approach could be easily adopted by other crop models.
Cited articles
Allegrini, J. and Carmeliet, J.: Coupled CFD and building energy simulations
for studying the impacts of building height topology and buoyancy on local
urban microclimates, Urban Climate, 21, 278–305,
https://doi.org/10.1016/j.uclim.2017.07.005, 2017. a, b
Arora, V. K. and Boer, G. J.: A parameterization of leaf phenology for the
terrestrial ecosystem component of climate models, Glob. Change Biol., 11,
39–59, 2005. a
Bastin, J.-F., Clark, E., Elliott, T., Hart, S., van den Hoogen, J., Hordijk,
I., Ma, H., Majumder, S., Manoli, G., Maschler, J., Mo, L., Routh, D., Yu,
K., Zohner, C., and Crowther, T. W.: Understanding climate change from a global analysis of city analogues, PLoS ONE, 14, e0217592, https://doi.org/10.1371/journal.pone.0217592, 2019. a
Berland, A., Shiflett, S. A., Shuster, W. D., Garmestani, A. S., Goddard,
H. C., Herrmann, D. L., and Hopton, M. E.: The role of trees in urban
stormwater management, Landscape Urban Plan., 162, 167–177,
https://doi.org/10.1016/j.landurbplan.2017.02.017, 2017. a
Best, M. J. and Grimmond, C. S. B.: Key conclusions of the first international
urban land surface model comparison project, B. Am. Meteorol.
Soc., 96, 805–819, https://doi.org/10.1175/BAMS-D-14-00122.1, 2015. a, b
Bonan, G. B., Lawrence, D. M., Swenson, S. C., Oleson, K. W., Jung, M.,
Lawrence, P. J., Levis, S., and Reichstein, M.: Improving canopy processes
in the Community Land Model version 4 (CLM4) using global flux fields
empirically inferred from FLUXNET data, J. Geophys. Res.,
116, 1–22, https://doi.org/10.1029/2010jg001593, 2011. a
Bowler, D. E., Buyung-Ali, L., Knight, T. M., and Pullin, A. S.: Urban
greening to cool towns and cities : A systematic review of the empirical
evidence, Landscape Urban Plan., 97, 147–155,
https://doi.org/10.1016/j.landurbplan.2010.05.006, 2010. a, b
Broadbent, A. M., Coutts, A. M., Nice, K. A., Demuzere, M., Krayenhoff, E. S., Tapper, N. J., and Wouters, H.: The Air-temperature Response to Green/blue-infrastructure Evaluation Tool (TARGET v1.0): an efficient and user-friendly model of city cooling, Geosci. Model Dev., 12, 785–803, https://doi.org/10.5194/gmd-12-785-2019, 2019a. a, b, c
Broadbent, A. M., Coutts, A. M., Tapper, N. J., and Demuzere, M.: The cooling
effect of irrigation on urban microclimate during heatwave conditions, Urban
Climate, 23, 309–329, https://doi.org/10.1016/j.uclim.2017.05.002, 2018b. a
Bruse, M. and Fleer, H.: Simulating surface-plant-air interactions inside
urban environments with a three dimensional numerical model, Environ.
Model. Softw., 13, 373–384, https://doi.org/10.1016/S1364-8152(98)00042-5,
1998. a, b
Choudhury, B. J. and Monteith, J. L.: A four-layer model for the heat budget of homogeneous land surfaces, Q. J. Roy. Meteor.
Soc., 114, 378–398, 1988. a
Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in
the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through
2012-12-31, Environmental Data Initiative,
https://doi.org/10.6073/pasta/fed17d67583eda16c439216ca40b0669, 2017. a
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and
environmental regulation of stomatal conductance, photosynthesis and
transpiration-A model that includes a laminar boundary-layer, Agr.
Forest Meteorol., 54, 107–136, 1991. a
Collatz, G. J., Ribas-Carbo, M., and Berry, J. A.: Coupled
photosynthesis-stomatal conductance model for leaves of C4 plants,
Aust. J. Plant Physiol., 19, 519–538, 1992. a
Collins, D. B. G. and Bras, R. L.: Plant rooting strategies in water-limited
ecosystems, Water Resour. Res., 43, https://doi.org/10.1029/2006WR005541, 2007. a
Dai, Y., Dickinson, R. E., and Wang, Y.-P.: A two-big-leaf model for canopy
temperature, photosynthesis, and stomatal conductance, J. Climate,
17, 2281–2299, 2004. a
Deardorff, J. W.: Efficient prediction of ground surface temperature and
moisture with inclusion of a layer of vegetation, J. Geophys.
Res., 83, 1889–1903, 1978. a
de Munck, C., Lemonsu, A., Masson, V., Le Bras, J., and Bonhomme, M.:
Evaluating the impacts of greening scenarios on thermal comfort and energy
and water consumptions for adapting Paris city to climate change, Urban
Climate, 23, 260–286, https://doi.org/10.1016/j.uclim.2017.01.003, 2018. a
Demuzere, M., Harshan, S., Jaervi, L., Roth, M., Grimmond, C. S. B., Masson,
V., Oleson, K. W., Velasco, E., and Wouters, H.: Impact of urban canopy
models and external parameters on the modelled urban energy balance in a
tropical city, Q. J. Roy. Meteor. Soc., 143, 1581–1596, https://doi.org/10.1002/qj.3028, 2017. a, b, c, d, e, f, g, h, i, j, k
de Vries, D. A.: Thermal Properties of Soils, in: Physics of the Plant
Environment, edited by: van Wijk, W., North-Holland, Amsterdam, 1963. a
Farouki, O. T.: The thermal properties of soils in cold regions, Cold Reg.
Sci. Technol., 5, 67–75, 1981. a
Farquhar, G. D., Caemmerer, S. V., and Berry, J. A.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149,
78–90, 1980. a
Fatichi, S. and Pappas, C.: Constrained variability of modeled T:ET ratio
across biomes, Geophys. Res. Lett., 44, 6795–6803,
https://doi.org/10.1002/2017GL074041, 2017. a, b
Fatichi, S., Ivanov, V. Y., and Caporali, E.: Simulation of future climate
scenarios with a weather generator, Adv. Water Resour., 34,
448–467, https://doi.org/10.1016/j.advwatres.2010.12.013, 2011. a
Fatichi, S., Ivanov, V. Y., and Caporali, E.: A mechanistic ecohydrological
model to investigate complex interactions in cold and warm water-controlled
environments: 1. Theoretical framework and plot-scale analysis, J.
Adv. Model. Earth Syst., 4, M5002, https://doi.org/10.1029/2011MS000086,
2012a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Fatichi, S., Ivanov, V. Y., and Caporali, E.: A mechanistic ecohydrological
model to investigate complex interactions in cold and warm water-controlled
environments: 2. Spatiotemporal analyses, J.
Adv. Model. Earth Syst., 4, M5003, https://doi.org/10.1029/2011MS000087, 2012b. a, b, c, d, e, f, g, h, i, j, k, l, m
Frank, A., Heidemann, W., and Spindler, K.: Modeling of the surface-to-surface radiation exchange using a Monte Carlo method, in: J. Phys. Conf. Ser., 745, 032143, https://doi.org/10.1088/1742-6596/745/3/032143, 2016. a
Gillner, S., Vogt, J., Tharang, A., Dettmann, S., and Roloff, A.: Role of
street trees in mitigating effects of heat and drought at highly sealed urban
sites, Landscape Urban Plan., 143, 33–42,
https://doi.org/10.1016/j.landurbplan.2015.06.005, 2015. a
Golasi, I., Salata, F., de Lieto Vollaro, E., and Coppi, M.: Complying with the demand of standardization in outdoor thermal comfort: a first approach to the Global Outdoor Comfort Index (GOCI), Buil. Environ., 130, 104–119, https://doi.org/10.1016/j.buildenv.2017.12.021, 2018. a
Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., and Briggs, J. M.: Global Change and the Ecology of Cities, Science, 39, 756–760, 2008. a
Grimmond, C. S. B., Blackett, M., Best, M. J., Baik, J., Belcher, S. E.,
Beringer, J., Bohnenstengel, S. I., Calmet, I., Chen, F., Coutts, A., Dandou,
A., Fortuniak, K., Gouvea, M. L., Hamdi, R., Hendry, M., Kanda, M., Kawai,
T., Kawamoto, Y., Kondo, H., Krayenhoff, E. S., Lee, S., Loridan, T.,
Martilli, A., Masson, V., Miao, S., Oleson, K., Ooka, R., Pigeon, G., Porson,
A., Ryu, Y., Salamanca, F., Steeneveld, G. J., and Tombrou, M.: Initial
results from Phase 2 of the international urban energy balance model
comparison, Int. J. Climatol., 272, 244–272,
https://doi.org/10.1002/joc.2227, 2011. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Hadley, S. W., Erickson III, D. J., Hernandez, J. L., Broniak, C. T., and
Blasing, T. J.: Responses of energy use to climate change: A climate
modeling study, Geophys. Res. Lett., 33, 2–5,
https://doi.org/10.1029/2006GL026652, 2006. a
Haghighi, E., Shahraeeni, E., Lehmann, P., and Or, D.: Evaporation rates across a convective air boundary layer are dominated by diffusion, Water Resour. Res., 49, 1602–1610, https://doi.org/10.1002/wrcr.20166, 2013. a, b
Hillel, D.: Environmental Soil Physics: Fundamentals, Applications, and
Environmental Considerations, Academic Press, London, UK, 1998. a
Holst, C. C., Tam, C.-Y., and Chan, J. C. L.: Sensitivity of urban rainfall to anthropogenic heat flux: A numerical experiment, Geophys. Res.
Lett., 43, 2240–2248, https://doi.org/10.1002/2015GL067628.Received, 2016. a
Höppe, P.: The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment,
Int. J. Biometeorol., 43, 71–75, https://doi.org/10.1007/s004840050118, 1999. a
Huang, C.-W., Domec, J.-C., Ward, E. J., Duman, T., Manoli, G., Parolari,
A. J., and Katul, G. G.: The effect of plant water storage on water fluxes
within the coupled soil-plant system, New Phytol., 213, 1093–1106,
https://doi.org/10.1111/nph.14273, 2017. a
Iio, A., Hikosaka, K., Anten, N. P., Nakagawa, Y., and Ito, A.: Global
dependence of field-observed leaf area index in woody species on climate: A
systematic review, Global Ecol. Biogeogr., 23, 274–285,
https://doi.org/10.1111/geb.12133, 2014. a
IPCC: Climate Change 2014, Synthesis Report, Summary for Policymakers, 2014. a
Ivanov, V. Y., Bras, R. L., and Vivoni, E. R.: Vegetation-hydrology dynamics in complex terrain of semiarid areas: 1. A mechanistic approach to modeling
dynamic feedbacks, Water Resour. Res., 44, W03429, https://doi.org/10.1029/2006WR005588,
2008. a, b
Jochner, S., Alves-Eigenheer, M., Menzel, A., and Morellato, L. P. C.: Using
phenology to assess urban heat islands in tropical and temperate regions,
Int. J. Climatol., 33, 3141–3151, https://doi.org/10.1002/joc.3651,
2013. a
Kattge, J., Knorr, W., Raddatz, T., and Wirth, C.: Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale
terrestrial biosphere models, Glob. Change Biol., 15, 976–991,
https://doi.org/10.1111/j.1365-2486.2008.01744.x, 2009. a
Kent, C. W., Grimmond, S., and Gatey, D.: Aerodynamic roughness parameters in
cities: Inclusion of vegetation, J. Wind Eng. Ind. Aerod., 169, 168–176, https://doi.org/10.1016/j.jweia.2017.07.016, 2017. a
Konarska, J., Holmer, B., Lindberg, F., and Thorsson, S.: Influence of vegetation and building geometry on the spatial variations of air temperature
and cooling rates in a high-latitude city, Int. J.
Climatol., 36, 2379–2395, https://doi.org/10.1002/joc.4502, 2016. a
Krayenhoff, E. S., Christen, A., Martilli, A., and Oke, T. R.: A Multi-layer
Radiation Model for Urban Neighbourhoods with Trees, Bound.-Lay.
Meteorol., 151, 139–178, https://doi.org/10.1007/s10546-013-9883-1, 2014. a, b
Krayenhoff, E. S., Santiago, J.-L., Martilli, A., Christen, A., and Oke, T.:
Parametrization of Drag and Turbulence for Urban Neighbourhoods with Trees,
Bound.-Lay. Meteorol., 156, 157–189, https://doi.org/10.1007/s10546-015-0028-6,
2015. a
Lawrence, D. M., Levis, S., Zeng, X., Flanner, M. G., Bonan, G. B., Oleson,
K. W., Swenson, S. C., Lawrence, D. M., Sakaguchi, K., Slater, A. G., Yang,
Z.-L., Lawrence, P. J., and Thornton, P. E.: Parameterization improvements
and functional and structural advances in Version 4 of the Community Land
Model, J. Adv. Model. Earth Syst., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011. a
Lee, H. S., Matthews, C. J., Braddock, R. D., Sander, G. C., and Gandola, F.: A MATLAB method of lines template for transport equations, Environ.
Model. Softw., 19, 603–614, https://doi.org/10.1016/j.envsoft.2003.08.017, 2004. a
Lemonsu, A., Masson, V., Shashua-Bar, L., Erell, E., and Pearlmutter, D.: Inclusion of vegetation in the Town Energy Balance model for modelling urban green areas, Geosci. Model Dev., 5, 1377–1393, https://doi.org/10.5194/gmd-5-1377-2012, 2012. a, b
Leuning, R.: A critical appraisal of a combined stomatal- photosynthesis model for C3 plants, Plant Cell Environ., 18, 357–364, 1995. a
Leuning, R., Kelliher, F. M., Pury, D. G. G., and Schulze, E.-D.: Leaf
nitrogen, photosynthesis, conductance and transpiration: Scaling from leaves
to canopies, Plant, Cell Environ., 18, 1183–1200, 1995. a
Li, D. and Bou-Zeid, E.: Synergistic Interactions between Urban Heat Islands
and Heat Waves : The Impact in Cities Is Larger than the Sum of Its Parts,
J. Appl. Meteorol. Climatol., 52, 2051–2064,
https://doi.org/10.1175/JAMC-D-13-02.1, 2013. a
Li, D., Bou-Zeid, E., and Oppenheimer, M.: The effectiveness of cool and green roofs as urban heat island mitigation strategies, Environ. Res.
Lett., 9, 055002, https://doi.org/10.1088/1748-9326/9/5/055002, 2014. a
Lim, H. S. and Lu, X. X.: Sustainable urban stormwater management in the
tropics : An evaluation of Singapore's ABC Waters Program, J.
Hydrol., 538, 842–862, https://doi.org/10.1016/j.jhydrol.2016.04.063, 2016. a, b
Lindberg, F., Holmer, B., and Thorsson, S.: SOLWEIG 1.0 – Modelling spatial
variations of 3D radiant fluxes and mean radiant temperature in complex urban
settings, Int. J. Biometeorol., 52, 697–713,
https://doi.org/10.1007/s00484-008-0162-7, 2008. a, b
Liu, X., Li, X.-x., Harshan, S., Roth, M., and Velasco, E.: Evaluation of an
urban canopy model in a tropical city : the role of tree evapotranspiration, Environ. Res. Letters, 12, 094008, https://doi.org/10.1088/1748-9326/aa7ee7, 2017. a, b, c, d
Macdonald, R. W., Griffiths, R. F., and Hall, D. J.: An improved method for
the estimation of surface roughness of obstacle arrays, Atmos.
Environ., 32, 1857–1864, 1998. a
Mahat, V., Tarboton, D. G., and Molotch, N. P.: Testing above- and
below-canopy representations of turbulent fluxes in an energy balance
snowmelt model, Water Resour. Res., 49, 1107–1122,
https://doi.org/10.1002/wrcr.20073, 2013. a, b
Manickathan, L., Defraeye, T., Allegrini, J., Derome, D., and Carmeliet, J.:
Parametric study of the influence of environmental factors and tree
properties on the transpirative cooling effect of trees, Agr.
Forest Meteorol., 248, 259–274, https://doi.org/10.1016/j.agrformet.2017.10.014, 2018. a, b
Manoli, G., Ivanov, V. Y., and Fatichi, S.: Dry-Season Greening and Water
Stress in Amazonia: The Role of Modeling Leaf Phenology, J.
Geophys. Res.-Biogeo., 123, 1909–1926,
https://doi.org/10.1029/2017JG004282, 2018. a
Manoli, G., Fatichi, S., Schläpfer, M., Yu, K., Crowther, T. W., Meili,
N., Burlando, P., Katul, G. G., and Bou-Zeid, E.: Magnitude of urban heat
islands largely explained by climate and population, Nature, 573, 55–60,
https://doi.org/10.1038/s41586-019-1512-9, 2019. a
Mascart, P., Noilhan, J., and Giordani, H.: A Modified Parameterization of
Flux-Profile Relationships in the Surface Layer Using Different Roughness
Length Values for Heat and Momentum, Bound.-Lay. Meteorol., 72,
331–344, 1995. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
Matzarakis, A., Rutz, F., and Mayer, H.: Modelling radiation fluxes in simple and complex environments – application of the RayMan model, Int. J. Biometeorol., 51, 323–334, https://doi.org/10.1007/s00484-009-0261-0, 2007. a, b
Matzarakis, A., Rutz, F., and Mayer, H.: Modelling radiation fluxes in simple and complex environments: basics of the RayMan model, Int. J. Biometeorol., 54, 131–139, https://doi.org/10.1007/s00484-009-0261-0, 2010. a
Meili, N. and Fatichi, S.: Urban Tethys-Chloris (UT&C v1.0) with the
possibility of sub-hourly time steps, Zenodo, https://doi.org/10.5281/zenodo.3548147, 2019. a
Middel, A., Chhetri, N., and Quay, R.: Urban forestry and cool roofs:
Assessment of heat mitigation strategies in Phoenix residential
neighborhoods, Urban For. Urban Gree., 14, 178–186,
https://doi.org/10.1016/j.ufug.2014.09.010, 2015. a
Mirfenderesgi, G., Bohrer, G., Matheny, A., Fatichi, S., Frasson, R. P. D. M., and Schafer, K. V. R.: Tree-level hydrodynamic approach for modeling
aboveground water storage and stomatal conductance illuminates the effects of
tree hydraulic strategy, J. Geophys. Res.-Biogeo., 121,
1792–1813, https://doi.org/10.1002/2016JG003467, 2016. a
Mitchell, D., Heaviside, C., Vardoulakis, S., Huntingford, C., Masato, G., P
Guillod, B., Frumhoff, P., Bowery, A., Wallom, D., and Allen, M.:
Attributing human mortality during extreme heat waves to anthropogenic
climate change, Environ. Res. Lett., 11, 074006,
https://doi.org/10.1088/1748-9326/11/7/074006, 2016. a
Monteith, J. L.: Principles of Environmental Physics, Edward Arnold, London,
1973. a
Mora, C., Dousset, B., Caldwell, I. R., Powell, F. E., Geronimo, R. C.,
Bielecki, C. R., Counsell, C. W. W., Dietrich, B. S., Johnston, E. T., Louis,
L. V., Lucas, M. P., Mckenzie, M. M., Shea, A. G., Tseng, H., Giambelluca,
T. W., Leon, L. R., Hawkins, E., and Trauernicht, C.: Global risk of deadly
heat, Nat. Clim. Change, 7, 501–506, https://doi.org/10.1038/NCLIMATE3322, 2017. a
Ng, K. S. T., Sia, A., Ng, M. K., Tan, C. T., Chan, H. Y., Tan, C. H., Rawtaer, I., Feng, L., Mahendran, R., Larbi, A., Kua, E. H., and Ho, R. C.: Effects of horticultural therapy on asian older adults: A randomized controlled trial, Int. J. Environ. Res. Pub. He.,
15, 1–14, https://doi.org/10.3390/ijerph15081705, 2018. a
Nice, K. A., Coutts, A. M., and Tapper, N. J.: Development of the VTUF-3D v1.0 urban micro-climate model to support assessment of urban vegetation
influences on human thermal comfort, Urban Climate, 24, 1052–1076,
https://doi.org/10.1016/j.uclim.2017.12.008, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Noilhan, J. and Planton, S.: A simple parameterization of land surface
processes for meteorological models, Mon. Weather Rev., 117, 536–549,
1989. a
Nowak, D. J. and Crane, D. E.: Carbon storage and sequestration by urban trees in the USA, Environ. Pollut., 116, 381–389, 2002. a
Núnez, C. M., Varas, E. A., and Meza, F. J.: Modelling soil heat flux,
Theor. Appl. Climatol., 100, 251–260,
https://doi.org/10.1007/s00704-009-0185-y, 2010. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Kowen, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C., and Thornton, P. E.: Technical Description of version 4.5 of the Community Land
Model (CLM), Tech. Rep. NCAR/TN-503+STR, Natl. Cent. for Atmos. Res.,
Boulder, Colorado, 2013. a
Park, S.-U. and Lee, S.-H.: A Vegetated Urban Canopy Model for Meteorological and Environmental Modelling, Bound.-Lay. Meteorol., 126, 73–102, https://doi.org/10.1007/s10546-007-9221-6, 2008. a, b, c
Paschalis, A., Fatichi, S., Pappas, C., and Or, D.: Covariation of vegetation and climate constrains present and future T/ET variability, Environ. Res. Lett, 13, 104012, https://doi.org/10.1088/1748-9326/aae267, 2018. a
Pataki, D. E., Carreiro, M. M., Cherrier, J., Grulke, N. E., Jennings, V.,
Pincetl, S., Pouyat, R. V., Whitlow, T. H., and Zipperer, W. C.: Coupling
biogeochemical cycles in urban environments: Ecosystem services, green
solutions, and misconceptions, Front. Ecol. Environ., 9,
27–36, https://doi.org/10.1890/090220, 2011. a
Ramamurthy, P. and Bou-Zeid, E.: Contribution of impervious surfaces to urban evaporation, Water Resour. Res., 50, 2889–2902,
https://doi.org/10.1111/j.1752-1688.1969.tb04897.x, 2014. a
Ramamurthy, P., Bou-Zeid, E., Smith, J. A., Wang, Z., Baeck, M. L., Saliendra, N. Z., Hom, J. L., and Welty, C.: Influence of subfacet heterogeneity and material properties on the urban surface energy budget, J. Appl. Meteorol. Clim., 53, 2114–2129, https://doi.org/10.1175/JAMC-D-13-0286.1,
2014. a
Redon, E. C., Lemonsu, A., Masson, V., Morille, B., and Musy, M.: Implementation of street trees within the solar radiative exchange parameterization of TEB in SURFEX v8.0, Geosci. Model Dev., 10, 385–411, https://doi.org/10.5194/gmd-10-385-2017, 2017. a, b
Roth, M.: Review of urban climate research in (sub)tropical regions,
Int. J. Climate, 27, 1859–1873, https://doi.org/10.1002/joc, 2007. a
Rowley, F. B. and Eckley, W. A.: Surface coefficients as affected by wind
direction, ASHREA Trans., 39, 33–46, 1932. a
Rowley, F. B., Algren, A. B., and Blackshaw, J.: Surface conductance as
affected by air velocity, temperature and character of surface, ASHREA
Trans., 36, 429–446, 1930. a
Rutter, A. J., Morton, A. J., and Robins, P. C.: A predictive model of rainfall interception in forests. 2. Generalization of model and comparison with
observations in some coniferous and hardwood stands, J. Appl.
Ecol., 12, 367–380, 1975. a
Sailor, D. J. and Lu, L.: A top-down methodology for developing diurnal and
seasonal anthropogenic heating profiles for urban areas, Atmos.
Environ., 38, 2737–2748, https://doi.org/10.1016/j.atmosenv.2004.01.034, 2004. a
Sailor, D. J., Georgescu, M., Milne, J. M., and Hart, M. A.: Development of a national anthropogenic heating database with an extrapolation for international cities, Atmos. Environ., 118, 7–18,
https://doi.org/10.1016/j.atmosenv.2015.07.016, 2015. a
Salmond, J. A., Tadaki, M., Vardoulakis, S., Arbuthnott, K., Coutts, A.,
Demuzere, M., Dirks, K. N., Heaviside, C., Lim, S., Macintyre, H., Mcinnes,
R. N., and Wheeler, B. W.: Health and climate related ecosystem services
provided by street trees in the urban environment, Environ. Health, 15, S36,
https://doi.org/10.1186/s12940-016-0103-6, 2016. a
Saxton, K. E. and Rawls, W. J.: Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions, Soil Sci. Soc. Am. J., 70, 1569–1578, https://doi.org/10.2136/sssaj2005.0117, 2006. a
Schenk, H. J. and Jackson, R. B.: The global biogeography of roots, Ecol.
Monogr., 72, 311–328, 2002. a
Sellers, P. J., Dickinson, R. E., Randall, D. A., Betts, A. K., Hall, F. G.,
Berry, J. A., Collatz, G. J., Denning, A. S., Mooney, H. A., Nobre, C. A.,
Sato, N., Field, C. B., and Henderson-Sellers, A.: Modeling the Exchanges of
Energy, Water and Carbon Between Continents and the Atmosphere, Science, 275,
502–509, 1997. a
Shuttleworth, W. J.: Terrestrial hydrometeorology, John Wiley & Sons, Ltd, 2012. a
Shuttleworth, W. J. and Gurney, R. J.: The theoretical relationship between
foliage temperature and canopy resistance in sparse crops, Q. J. Roy. Meteor. Soc., 116, 497–519, 1990. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3, NCAR Tech Note, 488–494,
https://doi.org/10.5065/D6DZ069T, 2008. a
Stavropulos-Laffaille, X., Chancibault, K., Brun, J.-M., Lemonsu, A., Masson, V., Boone, A., and Andrieu, H.: Improvements to the hydrological processes of the Town Energy Balance model (TEB-Veg, SURFEX v7.3) for urban modelling and impact assessment, Geosci. Model Dev., 11, 4175–4194, https://doi.org/10.5194/gmd-11-4175-2018, 2018. a, b
Stewart, I. D. and Oke, T. R.: Local climate zones for urban temperature
studies, B. Am. Meteorol. Soc., 93, 1879–1900, https://doi.org/10.1175/BAMS-D-11-00019.1,
2012. a, b, c
Templeton, N. P., Vivoni, E. R., Wang, Z. H., and Schreiner-McGraw, A. P.:
Quantifying Water and Energy Fluxes Over Different Urban Land Covers in
Phoenix, Arizona, J. Geophys. Res.-Atmos., 123,
2111–2128, https://doi.org/10.1002/2017JD027845, 2018. a
United Nations: World Urbanization Prospects, 2014. a
van Genuchten, M. T.: A closed-form equation for predicting the hydraulic
conductivity of unsaturated soils, Soil Sci. Soc. Am. J.,
44, 892–898, 1980. a
Volo, T. J., Vivoni, E. R., Martin, C. A., Earl, S., and Ruddell, B. L.:
Modelling soil moisture, water partitioning, and plant water stress under
irrigated conditions in desert Urban areas, Ecohydrology, 7, 1297–1313,
https://doi.org/10.1002/eco.1457, 2014. a
Wang, C., Wang, Z.-H., and Yang, J.: Cooling Effect of Urban Trees on the
Built Environment of Contiguous United States, Earth's Future,
1066–1081, https://doi.org/10.1029/2018EF000891, 2018. a
Wang, C., Wang, Z.-H., and Yang, J.: Urban water capacity: Irrigation for heat mitigation, Comput. Environ. Urban, 78, 101397,
https://doi.org/10.1016/j.compenvurbsys.2019.101397, 2019. a
Wang, Y.-P. and Leuning, R.: A two-leaf model for canopy conductance,
photosynthesis and portioning of available energy I: Model description
and comparison with a multi-layered model, Agr. Forest
Meteorol., 91, 89–111, 1998. a
Wang, Z.-H.: Geometric effect of radiative heat exchange in concave structure with application to heating of steel I-sections in fire, Int. J. Heat Mass. Tran., 53, 997–1003,
https://doi.org/10.1016/j.ijheatmasstransfer.2009.11.013, 2010. a
Wang, Z.-F.: Monte Carlo simulations of radiative heat exchange in a street
canyon with trees, Solar Energy, 110, 704–713,
https://doi.org/10.1016/j.solener.2014.10.012, 2014. a, b, c
Wang, Z.-H., Bou-Zeid, E., and Smith, J. A.: A Spatially-Analytical Scheme for Surface Temperatures and Conductive Heat Fluxes in Urban Canopy Models,
Bound.-Lay. Meteorol., 138, 171–193, https://doi.org/10.1007/s10546-010-9552-6,
2011. a
Ward, H. C., Kotthaus, S., Järvi, L., and Grimmond, C. S.: Surface Urban Energy and Water Balance Scheme (SUEWS): Development and evaluation at two UK sites, Urban Climate, 18, 1–32, https://doi.org/10.1016/j.uclim.2016.05.001, 2016. a, b, c
Willmott, C. J.: Some Comments on the Evaluation of Model Performance,
B. Am. Meteorol. Soc., 63, 1309–1313, https://doi.org/10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2, 1982. a
Wouters, H., Demuzere, M., Ridder, K. D., and Van Lipzig, N. P.: The impact of impervious water-storage parametrization on urban climate modelling, Urban Climate, 11, 24–50, https://doi.org/10.1016/j.uclim.2014.11.005, 2015. a, b, c
Wouters, H., Demuzere, M., Blahak, U., Fortuniak, K., Maiheu, B., Camps, J., Tielemans, D., and van Lipzig, N. P. M.: The efficient urban canopy dependency parametrization (SURY) v1.0 for atmospheric modelling: description and application with the COSMO-CLM model for a Belgian summer, Geosci. Model Dev., 9, 3027–3054, https://doi.org/10.5194/gmd-9-3027-2016, 2016. a
Wullschleger, S. D.: Biochemical Limitations to Carbon Assimilation in C3
Plants—A Retrospective Analysis of the A/C i Curves from 109 Species,
J. Exp. Bot., 44, 907–920, https://doi.org/10.1093/jxb/44.5.907,
1993. a
Yang, J. and Wang, Z. H.: Planning for a sustainable desert city: The
potential water buffering capacity of urban green infrastructure, Landscape Urban Plan., 167, 339–347, https://doi.org/10.1016/j.landurbplan.2017.07.014, 2017. a
Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., and Schneider, A.:
The footprint of urban climates on vegetation phenology, Geophys.
Res. Lett., 31, 10–13, https://doi.org/10.1029/2004GL020137, 2004. a
Zhou, S., Duursma, R. A., Medlyn, B. E., Kelly, J. W., and Prentice, I. C.:
How should we model plant responses to drought? An analysis of stomatal and
non-stomatal responses to water stress, Agr. Forest Meteorol.,
182–183, 204–214, https://doi.org/10.1016/j.agrformet.2013.05.009, 2013. a
Ziegler, A. D., Terry, J. P., Oliver, G. J., Friess, D. A., Chuah, C. J., Chow, W. T., and Wasson, R. J.: Increasing Singapore's resilience to drought,
Hydrol. Proc., 28, 4543–4548, https://doi.org/10.1002/hyp.10212, 2014. a
Short summary
We developed a novel urban ecohydrological model (UT&C v1.0) that is able to account for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements in three cities in different climates (Singapore, Melbourne, Phoenix) and can be used to assess urban climate mitigation strategies that aim at increasing or changing urban green cover.
We developed a novel urban ecohydrological model (UT&C v1.0) that is able to account for the...
