Articles | Volume 13, issue 11
https://doi.org/10.5194/gmd-13-5147-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5147-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
31 Oct 2020
Model evaluation paper |
| 31 Oct 2020
| 31 Oct 2020
Modeling land surface processes over a mountainous rainforest in Costa Rica using CLM4.5 and CLM5
Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843, USA
Gretchen R. Miller
Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843, USA
Anthony T. Cahill
Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843, USA
Luiza Maria T. Aparecido
Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX 77843, USA
School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
Georgianne W. Moore
Department of Ecosystem Science and Management, Texas A&M University, 2138 TAMU, College Station, TX 77843, USA
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Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
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Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Ajinkya G. Deshpande, Thomas W. Boutton, Ayumi Hyodo, Charles W. Lafon, and Georgianne W. Moore
Biogeosciences, 17, 5639–5653, https://doi.org/10.5194/bg-17-5639-2020, https://doi.org/10.5194/bg-17-5639-2020, 2020
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Wetland forests in the southern USA are threatened by changing climate and human-induced pressures. We used tree ring widths and C isotopes as indicators of forest growth and physiological stress, respectively, and compared these to past climate data. We observed that vegetation growing in the drier patches is susceptible to stress, while vegetation growth and physiology in wetter patches is less sensitive to unfavorable environmental conditions, highlighting the importance of optimal wetness.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
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We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
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MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
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An improved method of the Globally Resolved Energy Balance model by the Bayesian networks
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Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
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ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
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Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
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To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
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The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
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This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
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How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
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Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
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This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
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Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
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A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
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Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
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In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
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A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
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Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
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This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
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We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
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Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
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This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
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Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
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Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
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This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
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Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
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Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
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This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
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A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
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FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-68, https://doi.org/10.5194/gmd-2023-68, 2023
Revised manuscript accepted for GMD
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We develpoed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observation and reconstruction. Based on our analyses, the observed isotope-temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach for determining the onset date of the West African summer monsoon.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023, https://doi.org/10.5194/gmd-16-2343-2023, 2023
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A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-60, https://doi.org/10.5194/gmd-2023-60, 2023
Revised manuscript accepted for GMD
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This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
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This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023, https://doi.org/10.5194/gmd-16-2095-2023, 2023
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Climate projections of a high-CO2 future are highly uncertain. A new study provides a novel approach to identifying key regions that dynamically explain the model uncertainty. To yield an accurate estimate of the future North Atlantic carbon uptake, we find that a correct simulation of the upper- and interior-ocean volume transport at 25–30° N is key. However, results indicate that models rarely perform well for both indicators and point towards inconsistencies within the model ensemble.
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023, https://doi.org/10.5194/gmd-16-2077-2023, 2023
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We used Doppler lidar to evaluate the wind profiles generated by a weather forecast model. We first compared the Doppler lidar observations with co-located radiosonde profiles, and they agree well. The model performs best over marine and coastal locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi
Geosci. Model Dev., 16, 2055–2076, https://doi.org/10.5194/gmd-16-2055-2023, https://doi.org/10.5194/gmd-16-2055-2023, 2023
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Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
EGUsphere, https://doi.org/10.5194/egusphere-2023-675, https://doi.org/10.5194/egusphere-2023-675, 2023
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Noah-MP is one of the most widely-used community land surface models in the world, which is designed for applications ranging from uncoupled land-surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhances the model modularity, interoperability, and applicability.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2023-354, https://doi.org/10.5194/egusphere-2023-354, 2023
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The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This manuscript shows that the new wind model, URock, fits generally well with observations and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students and practitioners).
Maria Chara Karypidou, Stefan Pieter Sobolowski, Lorenzo Sangelantoni, Grigory Nikulin, and Eleni Katragkou
Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, https://doi.org/10.5194/gmd-16-1887-2023, 2023
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Southern Africa is listed among the climate change hotspots; hence, accurate climate change information is vital for the optimal preparedness of local communities. In this work we assess the degree to which regional climate models (RCMs) are influenced by the global climate models (GCMs) from which they receive their lateral boundary forcing. We find that although GCMs exert a strong impact on RCMs, RCMs are still able to display substantial improvement relative to the driving GCMs.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
Geosci. Model Dev., 16, 1937–1960, https://doi.org/10.5194/gmd-16-1937-2023, https://doi.org/10.5194/gmd-16-1937-2023, 2023
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In this paper we develop a methodology to model the spatial distribution of solar radiation received by land over mountainous terrain. The approach is designed to be used in Earth system models, where coarse grid cells hinder the description of fine-scale land–atmosphere interactions. We adopt a clustering algorithm to partition the land domain into a set of homogeneous sub-grid
tiles, and for each tile we evaluate solar radiation received by land based on terrain properties.
Cited articles
Ali, A. A., Xu, C., Rogers, A., Fisher, R. A., Wullschleger, S. D., Massoud, E. C., Vrugt, J. A., Muss, J. D., McDowell, N. G., Fisher, J. B., Reich, P. B., and Wilson, C. J.: A global scale mechanistic model of photosynthetic capacity (LUNA V1.0), Geosci. Model Dev., 9, 587–606, https://doi.org/10.5194/gmd-9-587-2016, 2016. a
Aparecido, L. M. T., Cahill, A. T., Miller, G. R., and Moore, G. W.: Leaf
surface traits and water storage retention affect photosynthetic responses to
leaf surface wetness among wet tropical forest and semiarid savanna plants,
Tree Physiol., 37, 1285–1300, https://doi.org/10.1093/treephys/tpx092, 2017. a
Aston, A.: Rainfall interception by eight small trees, J. Hydrol.,
42, 383–396, https://doi.org/10.1016/0022-1694(79)90057-X, 1979. a
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A.,
Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W.,
Paw, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala,
T., Wilson, K., and Wofsy, S.: FLUXNET: a new tool to study the temporal
and spatial variability of ecosystem–scale carbon dioxide, water vapor, and
energy flux densities, B. Am. Meteorol. Soc., 82,
2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001. a
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal
conductance and its contribution to the control of photosynthesis under
different environmental conditions, in: Progress in photosynthesis research, Springer, Dordrecht, 221–224, 1987. a
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N.,
Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A.,
Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis,
H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C.,
Woodward, F. I., and Papale, D.: Terrestrial gross carbon dioxide uptake:
global distribution and covariation with climate, Science, 329, 834–838,
https://doi.org/10.1126/science.1184984, 2010. a
Bonan, G. B. (Ed.): A land surface model (LSM version 1.0) for ecological,
hydrological, and atmospheric studies: Technical description and user's
guide, NCAR Technical Note NCAR/TN-417+STR, National Center for Atmospheric
Research, Boulder, CO, 150 pp., 1996. a
Bonan, G. B.: Forests and climate change: forcings, feedbacks, and the climate
benefits of forests, Science, 320, 1444–1449, https://doi.org/10.1126/science.1155121,
2008. a, b
Bonan, G. B., Lawrence, P. J., Oleson, K. W., Levis, S., Jung, M., Reichstein,
M., Lawrence, D. M., and Swenson, S. C.: Improving canopy processes in the
Community Land Model version 4 (CLM4) using global flux fields empirically
inferred from FLUXNET data, J. Geophys. Res.-Biogeo., 116, G02014, https://doi.org/10.1029/2010JG001593, 2011. a, b, c, d
Bonan, G. B., Oleson, K. W., Fisher, R. A., Lasslop, G., and Reichstein, M.:
Reconciling leaf physiological traits and canopy flux data: use of the TRY
and FLUXNET databases in the Community Land Model version 4, J. Geophys. Res.-Biogeo., 117, G02026, https://doi.org/10.1029/2011JG001913, 2012. a
Bonan, G. B., Williams, M., Fisher, R. A., and Oleson, K. W.: Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum, Geosci. Model Dev., 7, 2193–2222, https://doi.org/10.5194/gmd-7-2193-2014, 2014. a
Bonan, G. B., Patton, E. G., Harman, I. N., Oleson, K. W., Finnigan, J. J., Lu, Y., and Burakowski, E. A.: Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0), Geosci. Model Dev., 11, 1467–1496, https://doi.org/10.5194/gmd-11-1467-2018, 2018. a, b
Burns, S., Swenson, S., Wieder, W., Lawrence, D., Bonan, G. B., Knowles, J.,
and Blanken, P.: A comparison of the diel cycle of modeled and measured
latent heat flux during the warm season in a Colorado subalpine forest,
J. Adv. Model. Earth Sy., 10, 617–651,
https://doi.org/10.1002/2017MS001248, 2018. a, b, c, d, e
Buzan, J. R., Oleson, K., and Huber, M.: Implementation and comparison of a suite of heat stress metrics within the Community Land Model version 4.5, Geosci. Model Dev., 8, 151–170, https://doi.org/10.5194/gmd-8-151-2015, 2015. a
Cai, X., Yang, Z.-L., Xia, Y., Huang, M., Wei, H., Leung, L. R., and Ek, M. B.:
Assessment of simulated water balance from Noah, Noah-MP, CLM, and VIC over
CONUS using the NLDAS test bed,
J. Geophys. Res.-Atmos., 119, 13751–13770, https://doi.org/10.1002/2014JD022113, 2014. a
Chaney, N. W., Herman, J. D., Ek, M. B., and Wood, E. F.: Deriving global
parameter estimates for the Noah land surface model using FLUXNET and
machine learning, J. Geophys. Res.-Atmos., 121,
13218–13235, https://doi.org/10.1002/2016JD024821, 2016. a
Chen, Y., Yang, K., Zhou, D., Qin, J., and Guo, X.: Improving the Noah Land
Surface Model in arid regions with an appropriate parameterization of the
thermal roughness length, J. Hydrometeorol., 11, 995–1006,
https://doi.org/10.1175/2010JHM1185.1, 2010. a, b
Choudhury, B. J. and DiGirolamo, N. E.: A biophysical process-based estimate of
global land surface evaporation using satellite and ancillary data I. Model
description and comparison with observations, J. Hydrol., 205,
164–185, https://doi.org/10.1016/S0022-1694(97)00147-9, 1998. a
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis,
D. J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M.,
Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of
hydrologic processes in Earth System Models, Water Resour. Res., 51,
5929–5956, https://doi.org/10.1002/2015WR017096, 2015. a, b
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C.,
Jones, C. D., and Luke, C. M.: Sensitivity of tropical carbon to climate
change constrained by carbon dioxide variability, Nature, 494, 341,
https://doi.org/10.1038/nature11882, 2013. 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, https://doi.org/10.1175/1520-0442(2004)017<2281:ATMFCT>2.0.CO;2, 2004. a
De Pury, D. G. G. and Farquhar, G. D.: A commentary on the use of a sun/shade
model to scale from the leaf to a canopy,
Agr. Forest Meteorol., 95, 257–260,
https://doi.org/10.1016/S0168-1923(99)00038-6, 1999. a
Deardorff, J. W.: Efficient prediction of ground surface temperature and
moisture, with inclusion of a layer of vegetation,
J. Geophys. Res.-Oceans, 83, 1889–1903, https://doi.org/10.1029/JC083iC04p01889, 1978. a, b, c
Dickinson, R. E.: Land surface processes and climate–surface albedos and
energy balance, Adv. Geophys., 25, 305–353,
https://doi.org/10.1016/S0065-2687(08)60176-4, 1983. a
Dickinson, R. E., Kennedy, P., and Henderson-Sellers, A.: Biosphere-atmosphere
transfer scheme (BATS) version 1e as coupled to the NCAR community climate
model, National Center for Atmospheric Research, Climate and Global Dynamics
Division, Boulder, CO, https://doi.org/10.5065/D67W6959, 1993. a, b
Ehleringer, J. and Björkman, O.: Quantum yields for CO2
uptake in C3 and C4 plants: dependence on temperature,
CO2, and O2 concentration, Plant
Physiol., 59, 86–90, https://doi.org/10.1104/pp.59.1.86, 1977. a
Ehleringer, J. and Pearcy, R. W.: Variation in Quantum Yield for
CO2 Uptake among C3 and C4 Plants, Plant Physiol.,
73, 555–559, https://doi.org/10.1104/pp.73.3.555, 1983. a
Evans, J.: Improving photosynthesis., Plant Physiol., 162, 1780–1793,
https://doi.org/10.1104/pp.113.219006, 2013. a, b, c, d
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L.,
Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W.,
Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague, C.,
Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, J., Pelletier,
J., Perket, J., Rouholahnejad-Freund, E., Wagener, T., Zeng, X., Beighley,
E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B., Safeeq,
M., Shen, C., van Verseveld, W., Volk, J., and Yamazaki, D.: Hillslope
Hydrology in Global Change Research and Earth System Modeling, Water Resour. Res., 55, 1737–1772, https://doi.org/10.1029/2018WR023903,
2019a. a, b
Fan, Y., Meijide, A., Lawrence, D. M., Roupsard, O., Carlson, K. M., Chen,
H.-Y., Röll, A., Niu, F., and Knohl, A.: Reconciling Canopy Interception
Parameterization and Rainfall Forcing Frequency in the Community Land Model
for Simulating Evapotranspiration of Rainforests and Oil Palm Plantations in
Indonesia, J. Adv. Model. Earth Sy., 11, 732–751,
https://doi.org/10.1029/2018MS001490, 2019b. a, b, c
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3 species,
Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980. a
Fisher, J. B., Malhi, Y., Bonal, D., Rocha, H. R. D., Araujo, A. C. D., Gamo,
M., Goulden, M. L., Hirano, T., Huete, A. R., Kondo, H., Kumagai, T.,
Loescher, H. W., Miller, S., Nobre, A. D., Nouvellon, Y., Oberbauer, S. F.,
Panuthai, S., Roupsard, O., Saleska, S., Tanaka, K., Tanaka, N., Tu, K. P.,
and Randow, C. V.: The land–atmosphere water flux in the tropics,
Glob. Change Biol., 15, 2694–2714, https://doi.org/10.1111/j.1365-2486.2008.01813.x, 2009. a, b, c, d
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze,
M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence,
P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D.,
Muller-Landau, H., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K.,
Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu,
X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System
Models: A review of progress and priorities, Glob. Change Biol., 24,
35–54, https://doi.org/10.1111/gcb.13910, 2018. a
Giambelluca, T. W., Martin, R. E., Asner, G. P., Huang, M., Mudd, R. G.,
Nullet, M. A., DeLay, J. K., and Foote, D.: Evapotranspiration and energy
balance of native wet montane cloud forest in Hawai'i,
Agr. Forest Meteorol., 149, 230–243, https://doi.org/10.1016/j.agrformet.2008.08.004,
2009. a
Hasler, N. and Avissar, R.: What controls evapotranspiration in the Amazon
basin?, J. Hydrometeorol., 8, 380–395, https://doi.org/10.1175/JHM587.1,
2007. a, b
Heidkamp, M., Chlond, A., and Ament, F.: Closing the energy balance using a
canopy heat capacity and storage concept – a physically based approach for
the land component JSBACHv3.11, 11, 3465–3479,
https://doi.org/10.5194/gmd-11-3465-2018, 2018. a
Huntingford, C., Zelazowski, P., Galbraith, D., Mercado, L. M., Sitch, S.,
Fisher, R., Lomas, M., Walker, A. P., Jones, C. D., Booth, B. B. B., Malhi,
Y., Hemming, D., Kay, G., Good, P., Lewis, S. L., Phillips, O. L., Atkin,
O. K., Lloyd, J., Gloor, E., Zaragoza-Castells, J., Meir, P., Betts, R.,
Harris, P. P., Nobre, C., Marengo, J., and Cox, P. M.: Simulated resilience
of tropical rainforests to CO2-induced climate change,
Nat. Geosci., 6, 268–273, https://doi.org/10.1038/ngeo1741, 2013. a, b
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a, b
Katul, G., Manzoni, S., Palmroth, S., and Oren, R.: A stomatal optimization
theory to describe the effects of atmospheric CO2 on leaf
photosynthesis and transpiration, Ann. Bot., 105, 431–442, 2010. a
Lalic, B., Firanj, A., Mihailovic, D. T., and Podrascanin, Z.: Parameterization
of PAR vertical profile within horizontally uniform forest canopies for use
in environmental modeling, J. Geophys. Res.-Atmos., 118,
8156–8165, 2013. a
Larsen, M. A. D., Refsgaard, J. C., Jensen, K. H., Butts, M. B., Stisen, S.,
and Mollerup, M.: Calibration of a distributed hydrology and land surface
model using energy flux measurements, Agr. Forest Meteorol.,
217, 74–88, https://doi.org/10.1016/j.agrformet.2015.11.012, 2016. a
Launiainen, S., Katul, G. G., Kolari, P., Vesala, T., and Hari, P.: Empirical
and optimal stomatal controls on leaf and ecosystem level
CO2 and H2O exchange rates, Agr. Forest Meteorol., 151, 1672–1689,
https://doi.org/10.1016/j.agrformet.2011.07.001, 2011. a
Launiainen, S., Katul, G. G., Lauren, A., and Kolari, P.: Coupling boreal
forest CO2, H2O and energy flows by a
vertically structured forest canopy – soil model with separate bryophyte
layer, Ecol. Model., 312, 385–405,
https://doi.org/10.1016/j.ecolmodel.2015.06.007, 2015. a
Lawrence, D. and Vandecar, K.: Effects of tropical deforestation on climate and agriculture, Nat. Clim. Change, 5, 27–36, https://doi.org/10.1038/nclimate2430,
2014. a
Lawrence, D., Fisher, R., Koven, C., Oleson, K., Swenson, S., Vertenstein, M.,
Andre, B., Bonan, G. B., Ghimire, B., van Kampenhout, L., Kennedy, D.,
Kluzek, E., Knox, R., Lawrence, P., Li, F., Li, H., Lombardozzi, D., Lu, Y.,
Perket, J., Riley, W., Sacks, W., Shi, M., Wieder, W., Xu, C., Ali, A.,
Badger, A., Bisht, G., Broxton, P., Brunke, M., Buzan, J., Clark, M., Craig,
T., Dahlin, K., Drewniak, B., Emmons, L., Fisher, J., Flanner, M., Gentine,
P., Lenaerts, J., Levis, S., Leung, L. R., Lipscomb, W., Pelletier, J.,
Ricciuto, D. M., Sanderson, B., Shuman, J., Slater, A., Subin, Z., Tang, J.,
Tawfik, A., Thomas, Q., Tilmes, S., Vitt, F., and Zeng, X. (Eds.): Technical
Description of version 5.0 of the Community Land Model (CLM), National Center
for Atmospheric Research, Boulder, CO, ncar technical note edn., iD: 156,
2018. a, b, c
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson,
S. C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K.,
Bonan, G. B., and Slater, A. G.: Parameterization improvements and functional
and structural advances in Version 4 of the Community Land Model, J. Adv. Model. Earth Sy., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011. a, b
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. a
Maass, J., Vose, J. M., Swank, W. T., and Martínez-Yrízar, A.: Seasonal
changes of leaf area index (LAI) in a tropical deciduous forest in west
Mexico, Forest Ecol. Manag., 74, 171–180, 1995. a
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C.,
Barton, C. V. M., Crous, K. Y., Angelis, P., Freeman, M., and Wingate, L.:
Reconciling the optimal and empirical approaches to modelling stomatal
conductance, Glob. Change Biol., 17, 2134–2144,
https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011. a
Meier, R., Davin, E. L., Swenson, S. C., Lawrence, D. M., and Schwaab, J.:
Corrigendum: Biomass heat storage dampens diurnal temperature variations in
forests, Environ. Res. Lett., 14, 119502,
https://doi.org/10.1088/1748-9326/ab4a42, 2019. a
Michaletz, S. T., Weiser, M. D., McDowell, N. G., Zhou, J., Kaspari, M.,
Helliker, B. R., and Enquist, B. J.: The energetic and carbon economic
origins of leaf thermoregulation, Nat. Plants, 2, 16129,
https://doi.org/10.1038/nplants.2016.129, 2016. a
Moore, G. W., Orozco, G., Aparecido, L. M. T., and Miller, G. R.: Upscaling
transpiration in diverse forests: Insights from a tropical premontane site,
Ecohydrology, 11, e1920, https://doi.org/10.1002/eco.1920, 2018. a
Muñoz-Villers, L. E., Holwerda, F., Gómez-Cárdenas, M., Equihua, M.,
Asbjornsen, H., Bruijnzeel, L. A., Marín-Castro, B. E., and Tobón, C.:
Water balances of old-growth and regenerating montane cloud forests in
central Veracruz, Mexico, J. Hydrol., 462–463, 53–66,
https://doi.org/10.1016/j.jhydrol.2011.01.062, 2012. a
Oleson, K. W., Niu, G. Y., Yang, Z. L., Lawrence, D. M., Thornton, P. E.,
Lawrence, P. J., Stöckli, R., Dickinson, R. E., Bonan, G. B., Levis, S.,
Dai, A., and Qian, T.: Improvements to the Community Land Model and their
impact on the hydrological cycle, J. Geophys. Res.-Biogeo., 113, G01021, https://doi.org/10.1029/2007JG000563, 2008. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,
Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker,
M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque, J. F.,
Mahowald, N., Niu, G. Y., Qian, T., Randerson, J., Running, S., Sakaguchi,
K., Slater, A., Stockli, R., Wang, A., Yang, Z. L., Zeng, X., and Zeng, X.,
(Eds.): Technical description of version 4.0 of the Community Land Model
(CLM), National Center for Atmospheric Research, Boulder, CO, ncar technical
note ncar/tn-478+str edn., 2010. a, b, c
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J. F.,
Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S., Ricciuto, D. M.,
Sacks, W., Sun, Y., Tang, J., and Yang, Z. L. (Eds.): Technical Description of
Version 4.5 of the Community Land Model (CLM), National Center for
Atmospheric Research, Boulder, CO, ncar technical note ncar/tn-503 str edn.,
2013. a, b, c, d, e, f, g, h, i, j, k
Papale, D., Reichstein, M., Aubinet, M., Canfora, E., Bernhofer, C., Kutsch, W., Longdoz, B., Rambal, S., Valentini, R., Vesala, T., and Yakir, D.: Towards a standardized processing of Net Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation, Biogeosciences, 3, 571–583, https://doi.org/10.5194/bg-3-571-2006, 2006. a
Qian, T., Dai, A., Trenberth, K. E., and Oleson, K. W.: Simulation of global
land surface conditions from 1948 to 2004. Part I: forcing data and
evaluations, J. Hydrometeorol., 7, 953–975,
https://doi.org/10.1175/JHM540.1, 2006. a
Raj, R., Hamm, N. A. S., Tol, C. V. D., and Stein, A.: Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements, Biogeosciences, 13, 1409–1422, https://doi.org/10.5194/bg-13-1409-2016, 2016. a, b
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier,
P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T.,
Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila,
A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M.,
Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G.,
Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of
net ecosystem exchange into assimilation and ecosystem respiration: review
and improved algorithm, Glob. Change Biol., 11, 1424–1439,
https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005. a
Ryder, J., Polcher, J., Peylin, P., Ottlé, C., Chen, Y., van Gorsel, E., Haverd, V., McGrath, M. J., Naudts, K., Otto, J., Valade, A., and Luyssaert, S.: A multi-layer land surface energy budget model for implicit coupling with global atmospheric simulations, Geosci. Model Dev., 9, 223–245, https://doi.org/10.5194/gmd-9-223-2016, 2016. a, b
Sacks, B.: Community Terrestrial Systems Model, Zenodo, https://doi.org/10.5281/zenodo.3779821, 2020. a
Schlesinger, W. H. and Jasechko, S.: Transpiration in the global water cycle,
Agr. Forest Meteorol., 189, 115–117,
https://doi.org/10.1016/j.agrformet.2014.01.011, 2014. a
Sellers, P., Berry, J., Collatz, G., Field, C., and Hall, F.: Canopy
reflectance, photosynthesis, and transpiration. III. A reanalysis using
improved leaf models and a new canopy integration scheme, Remote Sens. Environ., 42, 187–216, https://doi.org/10.1016/0034-4257(92)90102-P, 1992. a
Sheil, D.: Forests, atmospheric water and an uncertain future: the new biology
of the global water cycle, Forest Ecosystems, 5, 19,
https://doi.org/10.1186/s40663-018-0138-y, 2018. a
Shuttleworth, W. J.: Evaporation from Amazonian rainforest, Proceedings –
Royal Society Biological Sciences, 233, 321–346,
https://doi.org/10.1098/rspb.1988.0024, 1988. a
Singh, R. S., Reager, J. T., Miller, N. L., and Famiglietti, J. S.: Toward
hyper-resolution land-surface modeling: The effects of fine-scale topography
and soil texture on CLM4.0 simulations over the Southwestern U.S., Water
Resour. Res., 51, 2648–2667, https://doi.org/10.1002/2014WR015686, 2015. a
Song, J.: Input forcing data for CLM4.5 or CLM5 at Soltis Center in Costa Rica (Version v0.0) [Data set], Zenodo, https://doi.org/10.5281/zenodo.3958253, 2020. a
Swenson, S. C. and Lawrence, D. M.: Assessing a dry surface layer-based soil
resistance parameterization for the Community Land Model using GRACE and
FLUXNET-MTE data, J. Geophys. Res.-Atmos., 119, 10299–10312, https://doi.org/10.1002/2014JD022314, 2014. a
Swenson, S. C., Burns, S. P., and Lawrence, D. M.: The Impact of Biomass Heat
Storage on the Canopy Energy Balance and Atmospheric Stability in the
Community Land Model, J. Adv. Model. Earth Sy., 11,
83–98, https://doi.org/10.1029/2018MS001476, 2019. a
Teale, N. G., Mahan, H., Bleakney, S., Berger, A., Shibley, N., Frauenfeld,
O. W., Quiring, S. M., Rapp, A. D., Roark, E. B., and Washington-Allen, R.:
Impacts of vegetation and precipitation on throughfall heterogeneity in a
tropical pre-montane transitional cloud forest, Biotropica, 46, 667–676,
https://doi.org/10.1111/btp.12166, 2014. a, b, c
Thornton, P. E., Lamarque, J.-F., Rosenbloom, N. A., and Mahowald, N. M.:
Influence of carbon-nitrogen cycle coupling on land model response to
CO2 fertilization and climate variability, Global Biogeochem. Cy., 21, GB4018, https://doi.org/10.1029/2006GB002868, 2007. a
Vose, J. M., Clinton, B. D., Sullivan, N. H., and Bolstad, P. V.: Vertical leaf
area distribution, light transmittance, and application of the Beer-Lambert
law in four mature hardwood stands in the southern Appalachians, Can. J. Forest Res., 25, 1036–1043, 1995. a
Wang, A., Barlage, M., Zeng, X., and Draper, C. S.: Comparison of land skin
temperature from a land model, remote sensing, and in situ measurement,
J. Geophys. Res.-Atmos., 119, 3093–3106,
https://doi.org/10.1002/2013JD021026, 2014. a, b, c
Wang, A., Zeng, X., and Guo, D.: Estimates of global surface hydrology and heat
fluxes from the Community Land Model (CLM4.5) with four atmospheric forcing
datasets, J. Hydrometeorol., 17, 2493–2510,
https://doi.org/10.1175/JHM-D-16-0041.1, 2016. a
Wang, Y. and Leuning, R.: A two-leaf model for canopy conductance,
photosynthesis and partitioning of available energy I:: Model description and
comparison with a multi-layered model, Agr. Forest Meteorol.,
91, 89–111, https://doi.org/10.1016/S0168-1923(98)00061-6, 1998. a
Wohl, E., Barros, A., Brunsell, N., Chappell, N. A., Coe, M., Giambelluca, T.,
Goldsmith, S., Harmon, R., Hendrickx, J. M. H., Juvik, J., McDonnell, J., and
Ogden, F.: The hydrology of the humid tropics, Nat. Clim. Change, 2, 655,
https://doi.org/10.1038/nclimate1556, 2012. a, b
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M.
F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D.,
van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade,
A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting
a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011. a
Yang, K., Koike, T., Fujii, H., Tamagawa, K., and Hirose, N.: Improvement of
surface flux parametrizations with a turbulence-related length, Q. J. Roy. Meteor. Soc., 128, 2073–2087,
https://doi.org/10.1256/003590002320603548, 2002. a
Zaehle, S. and Friend, A. D.: Carbon and nitrogen cycle dynamics in the O-CN
land surface model: 1. Model description, site-scale evaluation, and
sensitivity to parameter estimates, Global Biogeochem. Cy., 24, GB1005,
https://doi.org/10.1029/2009GB003521, 2010.
a
Zeng, X., Zhao, M., and Dickinson, R. E.: Intercomparison of bulk aerodynamic
algorithms for the computation of sea surface fluxes using TOGA COARE and
TAO data, J. Climate, 11, 2628–2644,
https://doi.org/10.1175/1520-0442(1998)011<2628:IOBAAF>2.0.CO;2, 1998. a
Zeng, X., Wang, Z., and Wang, A.: Surface skin temperature and the Interplay
between sensible and ground heat fluxes over arid regions, J. Hydrometeorol., 13, 1359–1370, https://doi.org/10.1175/JHM-D-11-0117.1, 2012. a, b
Zhang, K., Kimball, J. S., Nemani, R. R., and Running, S. W.: A continuous
satellite-derived global record of land surface evapotranspiration from 1983
to 2006, Water Resour. Res., 46, https://doi.org/10.1029/2009WR008800, 2010. a
Zheng, W., Wei, H., Wang, Z., Zeng, X., Meng, J., Ek, M., Mitchell, K., and
Derber, J.: Improvement of daytime land surface skin temperature over arid
regions in the NCEP GFS model and its impact on satellite data
assimilation, J. Geophys. Res.-Atmos., 117, D06117,
https://doi.org/10.1029/2011JD015901, 2012. a, b
Short summary
The performance of a land surface model (CLM4.5 and 5.0) was examined against a suite of measurements from a tropical montane rainforest in Costa Rica. Both versions failed to capture the effects of frequent rainfall events and mountainous terrain on temperature, leaf wetness, photosynthesis, and transpiration. While the new model version eliminated some errors, it still cannot precisely simulate a number of processes. This suggests that two key components of the model need modification.
The performance of a land surface model (CLM4.5 and 5.0) was examined against a suite of...
