Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4913-2022
© Author(s) 2022. 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-15-4913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Tree migration in the dynamic, global vegetation model LPJ-GM 1.1: efficient uncertainty assessment and improved dispersal kernels of European trees
Dynamic Macroecology/Land Change Science, Swiss Federal Institute for
Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
Veiko Lehsten
Dynamic Macroecology/Land Change Science, Swiss Federal Institute for
Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Department of Physical Geography and Ecosystem Science, Lund
University, Lund, Sweden
Heike Lischke
Dynamic Macroecology/Land Change Science, Swiss Federal Institute for
Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
Related authors
No articles found.
Joel Dawson White, Lena Ström, Veiko Lehsten, Janne Rinne, and Dag Ahrén
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-353, https://doi.org/10.5194/bg-2021-353, 2022
Revised manuscript not accepted
Short summary
Short summary
Microbes that produce CH4 play an important role to climate. Microbes which emit CH4 from wetlands is poorly understood. We observed that microbial community was of importance in explaining CH4 emission. We found, that microbes that produce CH4 hold the ability to produce and consume CH4 in multiple ways. This is important in terms of future climate scenarios, where wetlands are expected to shift. Therefore, we expect the community to be highly adaptive to future climate scenarios.
Matthias J. R. Speich, Massimiliano Zappa, Marc Scherstjanoi, and Heike Lischke
Geosci. Model Dev., 13, 537–564, https://doi.org/10.5194/gmd-13-537-2020, https://doi.org/10.5194/gmd-13-537-2020, 2020
Short summary
Short summary
Climate change is expected to substantially affect natural processes, and simulation models are a valuable tool to anticipate these changes. In this study, we combine two existing models that each describe one aspect of the environment: forest dynamics and the terrestrial water cycle. The coupled model better described observed patterns in vegetation structure. We also found that including the effect of water availability on tree height and rooting depth improved the model.
Veiko Lehsten, Michael Mischurow, Erik Lindström, Dörte Lehsten, and Heike Lischke
Geosci. Model Dev., 12, 893–908, https://doi.org/10.5194/gmd-12-893-2019, https://doi.org/10.5194/gmd-12-893-2019, 2019
Short summary
Short summary
To assess the effect of climate on vegetation, dynamic vegetation models simulate their response e.g. to climate change. Most currently used dynamic vegetation models ignore the fact that for colonization of a new area not only do the climatic conditions have to be suitable, but seeds also need to arrive at the site to allow the species to migrate there. In this paper we are developing a novel method which allows us to simulate migration within dynamic vegetation models even at large scale.
Matthias J. R. Speich, Heike Lischke, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 22, 4097–4124, https://doi.org/10.5194/hess-22-4097-2018, https://doi.org/10.5194/hess-22-4097-2018, 2018
Short summary
Short summary
To simulate the water balance of, e.g., a forest plot, it is important to estimate the maximum volume of water available to plants. This depends on soil properties and the average depth of roots. Rooting depth has proven challenging to estimate. Here, we applied a model assuming that plants dimension their roots to optimize their carbon budget. We compared its results with values obtained by calibrating a dynamic water balance model. In most cases, there is good agreement between both methods.
Florian Sallaba, Stefan Olin, Kerstin Engström, Abdulhakim M. Abdi, Niklas Boke-Olén, Veiko Lehsten, Jonas Ardö, and Jonathan W. Seaquist
Earth Syst. Dynam., 8, 1191–1221, https://doi.org/10.5194/esd-8-1191-2017, https://doi.org/10.5194/esd-8-1191-2017, 2017
Short summary
Short summary
The UN sustainable development goals for eradicating hunger are at high risk for failure in the Sahel. We show that the demand for food and feed biomass will begin to outstrip its supply in the 2040s if current trends continue. Though supply continues to increase it is outpaced by a greater increase in demand due to a combination of population growth and a shift to diets rich in animal proteins. This underscores the importance of policy interventions that would act to mitigate such developments.
Related subject area
Climate and Earth system modeling
UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model
Porting the WAVEWATCH III (v6.07) wave action source terms to GPU
Yeti 1.0: a generalized framework for constructing bottom-up emission inventories from traffic sources at road-link resolutions
Analysis of systematic biases in tropospheric hydrostatic delay models and construction of a correction model
A new precipitation emulator (PREMU v1.0) for lower-complexity models
Simulating marine neodymium isotope distributions using Nd v1.0 coupled to the ocean component of the FAMOUS–MOSES1 climate model: sensitivities to reversible scavenging efficiency and benthic source distributions
CMIP6 simulations with the compact Earth system model OSCAR v3.1
Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1
The pseudo-global-warming (PGW) approach: methodology, software package PGW4ERA5 v1.1, validation, and sensitivity analyses
AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics
Cell tracking of convective rainfall: sensitivity of climate-change signal to tracking algorithm and cell definition (Cell-TAO v1.0)
ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales
Ocean Modeling with Adaptive REsolution (OMARE; version 1.0) – refactoring the NEMO model (version 4.0.1) with the parallel computing framework of JASMIN – Part 1: Adaptive grid refinement in an idealized double-gyre case
Monthly-scale extended predictions using the atmospheric model coupled with a slab ocean
stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts
URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research
Combining regional mesh refinement with vertically enhanced physics to target marine stratocumulus biases as demonstrated in the Energy Exascale Earth System Model version 1
Evaluation of native Earth system model output with ESMValTool v2.6.0
WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer
The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system
Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Accelerated photosynthesis routine in LPJmL4
Improving scalability of Earth system models through coarse-grained component concurrency – a case study with the ICON v2.6.5 modelling system
Temperature forecasting by deep learning methods
Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios
Inclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic–boreal zone in CLM5.0-FATES-Hydro
Climate change projections of wet and dry extreme events in the Upper Jhelum Basin using a multivariate drought index: Evaluation of bias correction
Implementation and evaluation of the GEOS-Chem chemistry module version 13.1.2 within the Community Earth System Model v2.1
Understanding AMOC stability: the North Atlantic Hosing Model Intercomparison Project
Assessment of JSBACHv4.30 as a land component of ICON-ESM-V1 in comparison to its predecessor JSBACHv3.2 of MPI-ESM1.2
Importance of Ice Nucleation and Precipitation on Climate with the Parameterization of Unified Microphysics Across Scales version 1 (PUMASv1)
Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED)
Impact of increased resolution on the representation of the Canary upwelling system in climate models
Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI): protocol and initial results from the first simulations
Introducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0)
Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of North China
Advancing precipitation prediction using a new-generation storm-resolving model framework – SIMA-MPAS (V1.0): a case study over the western United States
SURFER v2.0: a flexible and simple model linking anthropogenic CO2 emissions and solar radiation modification to ocean acidification and sea level rise
A new bootstrap technique to quantify uncertainty in estimates of ground surface temperature and ground heat flux histories from geothermal data
Modeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamics
Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)
A modeling framework to understand historical and projected ocean climate change in large coupled ensembles
TriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular grids
Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1
The Moist Quasi-Geostrophic Coupled Model: MQ-GCM 2.0
Pace v0.1: A Python-based Performance-Portable Implementation of the FV3 Dynamical Core
Transport parameterization of the Polar SWIFT model (version 2)
Effects of complex terrain on the shortwave radiative balance: A sub–grid scale parameterization for the GFDL Land Model version 4.2
Analog data assimilation for the selection of suitable general circulation models
Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
Short summary
Short summary
Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Olawale James Ikuyajolu, Luke Van Roekel, Steven R. Brus, Erin E. Thomas, Yi Deng, and Sarat Sreepathi
Geosci. Model Dev., 16, 1445–1458, https://doi.org/10.5194/gmd-16-1445-2023, https://doi.org/10.5194/gmd-16-1445-2023, 2023
Short summary
Short summary
Wind-generated waves play an important role in modifying physical processes at the air–sea interface, but they have been traditionally excluded from climate models due to the high computational cost of running spectral wave models for climate simulations. To address this, our work identified and accelerated the computationally intensive section of WAVEWATCH III on GPU using OpenACC. This allows for high-resolution modeling of atmosphere–wave–ocean feedbacks in century-scale climate integrations.
Edward C. Chan, Joana Leitão, Andreas Kerschbaumer, and Timothy M. Butler
Geosci. Model Dev., 16, 1427–1444, https://doi.org/10.5194/gmd-16-1427-2023, https://doi.org/10.5194/gmd-16-1427-2023, 2023
Short summary
Short summary
Yeti is a Handbook Emission Factors for Road Transport-based traffic emission inventory written in the Python 3 scripting language, which adopts a generalized treatment for activity data using traffic information of varying levels of detail introduced in a systematic and consistent manner, with the ability to maximize reusability. Thus, Yeti has been conceived and implemented with a high degree of data and process symmetry, allowing scalable and flexible execution while affording ease of use.
Haopeng Fan, Siran Li, Zhongmiao Sun, Guorui Xiao, Xinxing Li, and Xiaogang Liu
Geosci. Model Dev., 16, 1345–1358, https://doi.org/10.5194/gmd-16-1345-2023, https://doi.org/10.5194/gmd-16-1345-2023, 2023
Short summary
Short summary
The traditional tropospheric zenith hydrostatic delay (ZHD) model's bias is usually thought negligible, yet it still reaches 10 mm sometimes and would lead to millimeter-level position errors for space geodetic observations. Therefore, we analyzed the bias’ characteristics and present a grid model to correct the traditional ZHD formula. When verifying the efficiency based on data from the ECMWF (European Centre for Medium-Range Weather Forecasts), ZHD biases were rectified by ~50 %.
Gang Liu, Shushi Peng, Chris Huntingford, and Yi Xi
Geosci. Model Dev., 16, 1277–1296, https://doi.org/10.5194/gmd-16-1277-2023, https://doi.org/10.5194/gmd-16-1277-2023, 2023
Short summary
Short summary
Due to computational limits, lower-complexity models (LCMs) were developed as a complementary tool for accelerating comprehensive Earth system models (ESMs) but still lack a good precipitation emulator for LCMs. Here, we developed a data-calibrated precipitation emulator (PREMU), a computationally effective way to better estimate historical and simulated precipitation by current ESMs. PREMU has potential applications related to land surface processes and their interactions with climate change.
Suzanne Robinson, Ruza F. Ivanovic, Lauren J. Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul J. Valdes
Geosci. Model Dev., 16, 1231–1264, https://doi.org/10.5194/gmd-16-1231-2023, https://doi.org/10.5194/gmd-16-1231-2023, 2023
Short summary
Short summary
We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (Nd v1.0). Nd fluxes from seafloor sediment and incorporation of Nd onto sinking particles represent the major global sources and sinks, respectively. However, model–data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Yann Quilcaille, Thomas Gasser, Philippe Ciais, and Olivier Boucher
Geosci. Model Dev., 16, 1129–1161, https://doi.org/10.5194/gmd-16-1129-2023, https://doi.org/10.5194/gmd-16-1129-2023, 2023
Short summary
Short summary
The model OSCAR is a simple climate model, meaning its representation of the Earth system is simplified but calibrated on models of higher complexity. Here, we diagnose its latest version using a total of 99 experiments in a probabilistic framework and under observational constraints. OSCAR v3.1 shows good agreement with observations, complex Earth system models and emerging properties. Some points for improvements are identified, such as the ocean carbon cycle.
Sandra L. LeGrand, Theodore W. Letcher, Gregory S. Okin, Nicholas P. Webb, Alex R. Gallagher, Saroj Dhital, Taylor S. Hodgdon, Nancy P. Ziegler, and Michelle L. Michaels
Geosci. Model Dev., 16, 1009–1038, https://doi.org/10.5194/gmd-16-1009-2023, https://doi.org/10.5194/gmd-16-1009-2023, 2023
Short summary
Short summary
Ground cover affects dust emissions by reducing wind flow over the immediate soil surface. This study reviews a method for estimating ground cover effects on wind erosion from satellite-detected terrain shadows. We conducted a case study for a US dust event using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Adding the shadow-based method for ground cover effects markedly improved simulated results and may lead to better dust modeling outcomes in vegetated drylands.
Roman Brogli, Christoph Heim, Jonas Mensch, Silje Lund Sørland, and Christoph Schär
Geosci. Model Dev., 16, 907–926, https://doi.org/10.5194/gmd-16-907-2023, https://doi.org/10.5194/gmd-16-907-2023, 2023
Short summary
Short summary
The pseudo-global-warming (PGW) approach is a downscaling methodology that imposes the large-scale GCM-based climate change signal on the boundary conditions of a regional climate simulation. It offers several benefits in comparison to conventional downscaling. We present a detailed description of the methodology, provide companion software to facilitate the preparation of PGW simulations, and present validation and sensitivity studies.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
Short summary
Short summary
We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev., 16, 851–867, https://doi.org/10.5194/gmd-16-851-2023, https://doi.org/10.5194/gmd-16-851-2023, 2023
Short summary
Short summary
Cell-tracking algorithms allow for the study of properties of a convective cell across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm's criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Cathy Hohenegger, Peter Korn, Leonidas Linardakis, René Redler, Reiner Schnur, Panagiotis Adamidis, Jiawei Bao, Swantje Bastin, Milad Behravesh, Martin Bergemann, Joachim Biercamp, Hendryk Bockelmann, Renate Brokopf, Nils Brüggemann, Lucas Casaroli, Fatemeh Chegini, George Datseris, Monika Esch, Geet George, Marco Giorgetta, Oliver Gutjahr, Helmuth Haak, Moritz Hanke, Tatiana Ilyina, Thomas Jahns, Johann Jungclaus, Marcel Kern, Daniel Klocke, Lukas Kluft, Tobias Kölling, Luis Kornblueh, Sergey Kosukhin, Clarissa Kroll, Junhong Lee, Thorsten Mauritsen, Carolin Mehlmann, Theresa Mieslinger, Ann Kristin Naumann, Laura Paccini, Angel Peinado, Divya Sri Praturi, Dian Putrasahan, Sebastian Rast, Thomas Riddick, Niklas Roeber, Hauke Schmidt, Uwe Schulzweida, Florian Schütte, Hans Segura, Radomyra Shevchenko, Vikram Singh, Mia Specht, Claudia Christine Stephan, Jin-Song von Storch, Raphaela Vogel, Christian Wengel, Marius Winkler, Florian Ziemen, Jochem Marotzke, and Bjorn Stevens
Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, https://doi.org/10.5194/gmd-16-779-2023, 2023
Short summary
Short summary
Models of the Earth system used to understand climate and predict its change typically employ a grid spacing of about 100 km. Yet, many atmospheric and oceanic processes occur on much smaller scales. In this study, we present a new model configuration designed for the simulation of the components of the Earth system and their interactions at kilometer and smaller scales, allowing an explicit representation of the main drivers of the flow of energy and matter by solving the underlying equations.
Yan Zhang, Xuantong Wang, Yuhao Sun, Chenhui Ning, Shiming Xu, Hengbin An, Dehong Tang, Hong Guo, Hao Yang, Ye Pu, Bo Jiang, and Bin Wang
Geosci. Model Dev., 16, 679–704, https://doi.org/10.5194/gmd-16-679-2023, https://doi.org/10.5194/gmd-16-679-2023, 2023
Short summary
Short summary
We construct a new ocean model, OMARE, that can carry out multi-scale ocean simulation with adaptive mesh refinement. OMARE is based on the refactorization of NEMO with a third-party, high-performance piece of middleware. We report the porting process and experiments of an idealized western-boundary current system. The new model simulates turbulent and temporally varying mesoscale and submesoscale processes via adaptive refinement. Related topics and future work with OMARE are also discussed.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev., 16, 705–717, https://doi.org/10.5194/gmd-16-705-2023, https://doi.org/10.5194/gmd-16-705-2023, 2023
Short summary
Short summary
To improve the numerical model predictability of monthly extended-range scales, we use the simplified slab ocean model (SOM) to restrict the complicated sea surface temperature (SST) bias from a 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have better performance than the WRF-ROMS, which has a significant impact on the atmosphere. For extreme weather events such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Markus Köhli, Martin Schrön, Steffen Zacharias, and Ulrich Schmidt
Geosci. Model Dev., 16, 449–477, https://doi.org/10.5194/gmd-16-449-2023, https://doi.org/10.5194/gmd-16-449-2023, 2023
Short summary
Short summary
In the last decades, Monte Carlo codes were often consulted to study neutrons near the surface. As an alternative for the growing community of CRNS, we developed URANOS. The main model features are tracking of particle histories from creation to detection, detector representations as layers or geometric shapes, a voxel-based geometry model, and material setup based on color codes in ASCII matrices or bitmap images. The entire software is developed in C++ and features a graphical user interface.
Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
Geosci. Model Dev., 16, 335–352, https://doi.org/10.5194/gmd-16-335-2023, https://doi.org/10.5194/gmd-16-335-2023, 2023
Short summary
Short summary
Models that are used to simulate and predict climate often have trouble representing specific cloud types, such as stratocumulus, that are particularly thin in the vertical direction. It has been found that increasing the model resolution can help improve this problem. In this paper, we develop a novel framework that increases the horizontal and vertical resolutions only for areas of the globe that contain stratocumulus, hence reducing the model runtime while providing better results.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev., 16, 315–333, https://doi.org/10.5194/gmd-16-315-2023, https://doi.org/10.5194/gmd-16-315-2023, 2023
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang
Geosci. Model Dev., 16, 199–209, https://doi.org/10.5194/gmd-16-199-2023, https://doi.org/10.5194/gmd-16-199-2023, 2023
Short summary
Short summary
More and more researchers use deep learning models to replace physics-based parameterizations to accelerate weather simulations. However, embedding the ML models within the weather models is difficult as they are implemented in different languages. This work proposes a coupling framework to allow ML-based parameterizations to be coupled with the Weather Research and Forecasting (WRF) model. We also demonstrate using the coupler to couple the ML-based radiation schemes with the WRF model.
Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi
Geosci. Model Dev., 16, 179–197, https://doi.org/10.5194/gmd-16-179-2023, https://doi.org/10.5194/gmd-16-179-2023, 2023
Short summary
Short summary
Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of the near-term climate and are useful for informing decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
Short summary
Short summary
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
Short summary
Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Leonidas Linardakis, Irene Stemmler, Moritz Hanke, Lennart Ramme, Fatemeh Chegini, Tatiana Ilyina, and Peter Korn
Geosci. Model Dev., 15, 9157–9176, https://doi.org/10.5194/gmd-15-9157-2022, https://doi.org/10.5194/gmd-15-9157-2022, 2022
Short summary
Short summary
In Earth system modelling, we are facing the challenge of making efficient use of very large machines, with millions of cores. To meet this challenge we will need to employ multi-level and multi-dimensional parallelism. Component concurrency, being a function parallel technique, offers an additional dimension to the traditional data-parallel approaches. In this paper we examine the behaviour of component concurrency and identify the conditions for its optimal application.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
Short summary
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Thomas Bossy, Thomas Gasser, and Philippe Ciais
Geosci. Model Dev., 15, 8831–8868, https://doi.org/10.5194/gmd-15-8831-2022, https://doi.org/10.5194/gmd-15-8831-2022, 2022
Short summary
Short summary
We developed a new simple climate model designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: calibration using Bayesian inference, the possibility of coupling with integrated assessment models, and the capacity to explore climate scenarios compatible with limiting climate impacts. Here, we describe the model and its calibration using the latest data from complex CMIP6 models and the IPCC AR6, and we assess its performance.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
Short summary
Short summary
In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-237, https://doi.org/10.5194/gmd-2022-237, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
Bias correction has become indispensable to climate model output as a post-processing step to render climate model 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).
Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Haipeng Lin, Elizabeth W. Lundgren, Steve Goldhaber, Steven R. H. Barrett, and Daniel J. Jacob
Geosci. Model Dev., 15, 8669–8704, https://doi.org/10.5194/gmd-15-8669-2022, https://doi.org/10.5194/gmd-15-8669-2022, 2022
Short summary
Short summary
We bring the state-of-the-science chemistry module GEOS-Chem into the Community Earth System Model (CESM). We show that some known differences between results from GEOS-Chem and CESM's CAM-chem chemistry module may be due to the configuration of model meteorology rather than inherent differences in the model chemistry. This is a significant step towards a truly modular Earth system model and allows two strong but currently separate research communities to benefit from each other's advances.
Laura Claire Jackson, Eduardo Alastrué de Asenjo, Katinka Bellomo, Gokhan Danabasoglu, Helmuth Haak, Aixue Hu, Johann Jungclaus, Warren Lee, Virna L. Meccia, Oleg Saenko, Andrew Shao, and Didier Swingedouw
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-277, https://doi.org/10.5194/gmd-2022-277, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
The Atlantic meridional overturning circulation (AMOC) has an important impact on the climate. There are theories that freshening of the ocean might cause the AMOC to cross a tipping point (TP) beyond which recovery is difficult, however it is unclear whether TP exist in global climate models. Here we outline a set of experiments designed to explore AMOC tipping points and sensitivity to additional freshwater input as part of the North Atlantic hosing model intercomparison project (NAHosMIP).
Rainer Schneck, Veronika Gayler, Julia E. M. S. Nabel, Thomas Raddatz, Christian H. Reick, and Reiner Schnur
Geosci. Model Dev., 15, 8581–8611, https://doi.org/10.5194/gmd-15-8581-2022, https://doi.org/10.5194/gmd-15-8581-2022, 2022
Short summary
Short summary
The versions of ICON-A and ICON-Land/JSBACHv4 used for this study constitute the first milestone in the development of the new ICON Earth System Model ICON-ESM. JSBACHv4 is the successor of JSBACHv3, and most of the parameterizations of JSBACHv4 are re-implementations from JSBACHv3. We assess and compare the performance of JSBACHv4 and JSBACHv3. Overall, the JSBACHv4 results are as good as JSBACHv3, but both models reveal the same main shortcomings, e.g. the depiction of the leaf area index.
Andrew Gettelman, Hugh Morrison, Trude Eidhammer, Katherine Thayer-Calder, Jian Sun, Richard Forbes, Zachary McGraw, Jiang Zhu, Trude Storelvmo, and John Dennis
EGUsphere, https://doi.org/10.5194/egusphere-2022-980, https://doi.org/10.5194/egusphere-2022-980, 2022
Short summary
Short summary
Clouds are a critical part of weather and climate prediction. In this work, we document updates and corrections to the description of clouds used in several Earth System Models. These updates include the ability to run the scheme on Graphics Processing Units (GPUs) and changes to the numerical description of precipitation, as well as a correction to ice number. There are big improvements in computational performance that can be achieved with GPU acceleration.
Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, and Douglas C. Morton
Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, https://doi.org/10.5194/gmd-15-8411-2022, 2022
Short summary
Short summary
We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.
Adama Sylla, Emilia Sanchez Gomez, Juliette Mignot, and Jorge López-Parages
Geosci. Model Dev., 15, 8245–8267, https://doi.org/10.5194/gmd-15-8245-2022, https://doi.org/10.5194/gmd-15-8245-2022, 2022
Short summary
Short summary
Increasing model resolution depends on the subdomain of the Canary upwelling considered. In the Iberian Peninsula, the high-resolution (HR) models do not seem to better simulate the upwelling indices, while in Morocco to the Senegalese coast, the HR models show a clear improvement. Thus increasing the resolution of a global climate model does not necessarily have to be the only way to better represent the climate system. There is still much work to be done in terms of physical parameterizations.
Jadwiga H. Richter, Daniele Visioni, Douglas G. MacMartin, David A. Bailey, Nan Rosenbloom, Brian Dobbins, Walker R. Lee, Mari Tye, and Jean-Francois Lamarque
Geosci. Model Dev., 15, 8221–8243, https://doi.org/10.5194/gmd-15-8221-2022, https://doi.org/10.5194/gmd-15-8221-2022, 2022
Short summary
Short summary
Solar climate intervention using stratospheric aerosol injection is a proposed method of reducing global mean temperatures to reduce the worst consequences of climate change. We present a new modeling protocol aimed at simulating a plausible deployment of stratospheric aerosol injection and reproducibility of simulations using other Earth system models: Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI).
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
Short summary
Short summary
The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Entao Yu, Rui Bai, Xia Chen, and Lifang Shao
Geosci. Model Dev., 15, 8111–8134, https://doi.org/10.5194/gmd-15-8111-2022, https://doi.org/10.5194/gmd-15-8111-2022, 2022
Short summary
Short summary
A large number of simulations are conducted to investigate how different physical parameterization schemes impact surface wind simulations under stable weather conditions over the coastal regions of North China using the Weather Research and Forecasting model with a horizontal grid spacing of 0.5 km. Results indicate that the simulated wind speed is most sensitive to the planetary boundary layer schemes, followed by short-wave/long-wave radiation schemes and microphysics schemes.
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda
Geosci. Model Dev., 15, 8135–8151, https://doi.org/10.5194/gmd-15-8135-2022, https://doi.org/10.5194/gmd-15-8135-2022, 2022
Short summary
Short summary
We focus on the recent development of a state-of-the-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the western USA. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.
Marina Martínez Montero, Michel Crucifix, Victor Couplet, Nuria Brede, and Nicola Botta
Geosci. Model Dev., 15, 8059–8084, https://doi.org/10.5194/gmd-15-8059-2022, https://doi.org/10.5194/gmd-15-8059-2022, 2022
Short summary
Short summary
We present SURFER, a lightweight model that links CO2 emissions and geoengineering to ocean acidification and sea level rise from glaciers, ocean thermal expansion and Greenland and Antarctic ice sheets. The ice sheet module adequately describes the tipping points of both Greenland and Antarctica. SURFER is understandable, fast, accurate up to several thousands of years, capable of emulating results obtained by state of the art models and well suited for policy analyses.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932, https://doi.org/10.5194/gmd-15-7913-2022, https://doi.org/10.5194/gmd-15-7913-2022, 2022
Short summary
Short summary
Inversions of subsurface temperature profiles provide past long-term estimates of ground surface temperature histories and ground heat flux histories at timescales of decades to millennia. Theses estimates complement high-frequency proxy temperature reconstructions and are the basis for studying continental heat storage. We develop and release a new bootstrap method to derive meaningful confidence intervals for the average surface temperature and heat flux histories from any number of profiles.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Wentao Zhang, Xiangjun Shi, and Chunsong Lu
Geosci. Model Dev., 15, 7751–7766, https://doi.org/10.5194/gmd-15-7751-2022, https://doi.org/10.5194/gmd-15-7751-2022, 2022
Short summary
Short summary
The two-moment bulk cloud microphysics scheme used in CAM6 was modified to consider the impacts of the ice-crystal size distribution shape parameter (μi). After that, how the μi impacts cloud microphysical processes and then climate simulations is clearly illustrated by offline tests and CAM6 model experiments. Our results and findings are useful for the further development of μi-related parameterizations.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
Short summary
Short summary
A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Aiko Voigt, Petra Schwer, Noam von Rotberg, and Nicole Knopf
Geosci. Model Dev., 15, 7489–7504, https://doi.org/10.5194/gmd-15-7489-2022, https://doi.org/10.5194/gmd-15-7489-2022, 2022
Short summary
Short summary
In climate science, it is helpful to identify coherent objects, for example, those formed by clouds. However, many models now use unstructured grids, which makes it harder to identify coherent objects. We present a new method that solves this problem by moving model data from an unstructured triangular grid to a structured cubical grid. We implement the method in an open-source Python package and show that the method is ready to be applied to climate model data.
Jérémy Bernard, Erwan Bocher, Elisabeth Le Saux Wiederhold, François Leconte, and Valéry Masson
Geosci. Model Dev., 15, 7505–7532, https://doi.org/10.5194/gmd-15-7505-2022, https://doi.org/10.5194/gmd-15-7505-2022, 2022
Short summary
Short summary
OpenStreetMap is a collaborative project aimed at creaing a free dataset containing topographical information. Since these data are available worldwide, they can be used as standard data for geoscience studies. However, most buildings miss the height information that constitutes key data for numerous fields (urban climate, noise propagation, air pollution). In this work, the building height is estimated using statistical modeling using indicators that characterize the building's environment.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
Short summary
Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
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
EGUsphere, https://doi.org/10.5194/egusphere-2022-943, https://doi.org/10.5194/egusphere-2022-943, 2022
Short summary
Short summary
It is hard for scientists to write efficient code which runs fast on all kinds of supercomputers. They like writing Python because it is easier to read and use. We re-wrote a Fortran code that simulates weather and climate into Python. The Python code re-writes itself to a much faster language to run on either normal processors or graphics cards. On one big computer system, our code is 3.5–4x faster on its graphics cards than the original code is on its processors.
Ingo Wohltmann, Daniel Kreyling, and Ralph Lehmann
Geosci. Model Dev., 15, 7243–7255, https://doi.org/10.5194/gmd-15-7243-2022, https://doi.org/10.5194/gmd-15-7243-2022, 2022
Short summary
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Enrico Zorzetto, Sergey Malyshev, Nathaniel Chaney, David Paynter, Raymond Menzel, and Elena Shevliakova
EGUsphere, https://doi.org/10.5194/egusphere-2022-770, https://doi.org/10.5194/egusphere-2022-770, 2022
Short summary
Short summary
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 partiton land domain in a set of homogeneous sub-grid “tiles”, and for each evaluate solar radiation receive by land based on terrain properties.
Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Pierre Le Bras, Valérie Monbet, Florian Sévellec, and Pierre Tandeo
Geosci. Model Dev., 15, 7203–7220, https://doi.org/10.5194/gmd-15-7203-2022, https://doi.org/10.5194/gmd-15-7203-2022, 2022
Short summary
Short summary
We present a new approach to validate numerical simulations of the current climate. The method can take advantage of existing climate simulations produced by different centers combining an analog forecasting approach with data assimilation to quantify how well a particular model reproduces a sequence of observed values. The method can be applied with different observations types and is implemented locally in space and time significantly reducing the associated computational cost.
Chahan M. Kropf, Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch
Geosci. Model Dev., 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022, https://doi.org/10.5194/gmd-15-7177-2022, 2022
Short summary
Short summary
Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
Cited articles
Albrich, K., Rammer, W., and Seidl, R.: Climate change causes critical
transitions and irreversible alterations of mountain forests, Glob. Change
Biol., 26, 4013–4027, https://doi.org/10.1111/gcb.15118, 2020.
Alexander, J. M., Chalmandrier, L., Lenoir, J., Burgess, T. I., Essl, F.,
Haider, S., Kueffer, C., McDougall, K., Nuñez, M. A., Pauchard, A.,
Rabitsch, W., Rew, L. J., Sanders, N. J., and Pellissier, L.: Lags in the
response of mountain plant communities to climate change, Glob. Change
Biol., 24, 563–579, https://doi.org/10.1111/gcb.13976, 2018.
Armstrong, E., Hopcroft, P. O., and Valdes, P. J.: A simulated Northern
Hemisphere terrestrial climate dataset for the past 60,000 years, Sci Data,
6, 1–16, https://doi.org/10.1038/s41597-019-0277-1, 2019.
Baskin, C. C. and Baskin, J. M. Seeds: ecology, biogeography, and,
evolution of dormancy and germination, Elsevier, https://doi.org/10.1016/B978-0-12-080260-9.X5000-3, 1998.
Beckman, N. G., Aslan, C. E., Rogers, H. S., Kogan, O., Bronstein, J. L.,
Bullock, J. M., Hartig, F., HilleRisLambers, J., Zhou, Y., Zurell, D.,
Brodie, J. F., Bruna, E. M., Cantrell, R. S., Decker, R. R., Efiom, E.,
Fricke, E. C., Gurski, K., Hastings, A., Johnson, J. S., Loiselle, B. A.,
Miriti, M. N., Neubert, M. G., Pejchar, L., Poulsen, J. R., Pufal, G.,
Razafindratsima, O. H., Sandor, M. E., Shea, K., Schreiber, S., Schupp, E.
W., Snell, R. S., Strickland, C., and Zambrano, J.: Advancing an
interdisciplinary framework to study seed dispersal ecology, AoB Plants, 12, https://doi.org/10.1093/aobpla/plz048, 2020.
Berg, M. P., Kiers, E. T., Driessen, G., Van Der Heijden, M., Kooi, B. W.,
Kuenen, F., Liefting, M., Verhoef, H. A., and Ellers, J.: Adapt or disperse:
understanding species persistence in a changing world, Glob. Change Biol.,
16, 587–598, https://doi.org/10.1111/j.1365-2486.2009.02014.x, 2010.
Binney, H. A., Willis, K. J., Edwards, M. E., Bhagwat, S. A., Anderson, P.
M., Andreev, A. A., Blaauw, M., Damblon, F., Haesaerts, P., Kienast, F.,
Kremenetski, K. V., Krivonogov, S. K., Lozhkin, A. V., MacDonald, G. M.,
Novenko, E. Y., Oksanen, P., Sapelko, T. V., Väliranta, M., and
Vazhenina, L.: The distribution of late-Quaternary woody taxa in northern
Eurasia: evidence from a new macrofossil database, Quaternary Sci. Rev., 28,
2445–2464, https://doi.org/10.1016/j.quascirev.2009.04.016, 2009.
Birks, H. J. B.: Contributions of Quaternary botany to modern ecology and
biogeography, Plant Ecol. Divers., 12, 189–385,
https://doi.org/10.1080/17550874.2019.1646831, 2019.
Birks, H. J. B. and Birks, H. H.: Biological responses to rapid climate
change at the Younger Dryas–Holocene transition at Kråkenes, western
Norway, Holocene, 18, 19–30, https://doi.org/10.1177/0959683607085572,
2008.
Briscoe, N. J., Elith, J., Salguero-Gómez, R., Lahoz-Monfort, J. J.,
Camac, J. S., Giljohann, K. M., Holde, M. H., Hradsky, B. A., Kearney, M.
R., McMahon, S. M., Phillips, B. L., Regan, T. J., Rhodes, J. R., Vesk, P.
A., Wintle, B. A., Yen, J. D. L., and Guillera-Arroita, G.: Forecasting
species range dynamics with process-explicit models: matching methods to
applications, Ecol. Lett., 22, 1940–1956,
https://doi.org/10.1111/ele.13348, 2019.
Bugmann, H.: On the ecology of mountainous forests in a changing climate: a simulation study, Doctoral dissertation, ETH Zurich, https://doi.org/10.3929/ethz-a-00094650, 1994.
Bullock, J. M. and Clarke, R. T.: Long distance seed dispersal by wind:
measuring and modelling the tail of the curve, Oecologia, 124, 506–521,
https://doi.org/10.1007/PL00008876, 2000.
Bullock, J. M., Mallada González, L., Tamme, R., Götzenberger, L.,
White, S. M., Pärtel, M., and Hooftman, D. A.: A synthesis of empirical
plant dispersal kernels, J. Ecol., 105, 6–19,
https://doi.org/10.1111/1365-2745.12666, 2017.
Cain, M. L., Damman, H., and Muir, A.: Seed dispersal and the Holocene
migration of woodland herbs, Ecol. Monogr, 68, 325–347,
https://doi.org/10.1890/0012-9615(1998)068[0325:SDATHM]2.0.CO;2, 1998.
Caswell, H., Lensink, R., and Neubert, M. G.: Demography and dispersal: life
table response experiments for invasion speed, Ecology, 84, 1968–1978,
https://doi.org/10.1890/02-0100, 2003.
Cheaib, A., Badeau, V., Boe, J., Chuine, I., Delire, C., Dufrêne, E.,
François, C., Gritti, E. S., Legay, M., Pagé, C., Thuiller, W.,
Viovy, N., and Leadley, P.: Climate change impacts on tree ranges: model
intercomparison facilitates understanding and quantification of uncertainty,
Ecol. Lett., 15, 533–544,
https://doi.org/10.1111/j.1461-0248.2012.01764.x, 2012.
Chećko, E., Jaroszewicz, B., Olejniczak, K., and
Kwiatkowska-Falińska, A. J.: The importance of coarse woody debris for
vascular plants in temperate mixed deciduous forests, Can. J. Forest Res.,
45, 1154–1163, https://doi.org/10.1139/cjfr-2014-0473, 2015.
Clark, J. S.: Why trees migrate so fast: confronting theory with dispersal
biology and the paleorecord, Am. Nat., 152, 204–224,
https://doi.org/10.1086/286162, 1998.
Clark, J. S., Silman, M., Kern, R., Macklin, E., and HilleRisLambers, J.:
Seed dispersal near and far: patterns across temperate and tropical forests,
Ecology, 80, 1475–1494,
https://doi.org/10.1890/0012-9658(1999)080[1475:SDNAFP]2.0.CO;2, 1999.
Clark, J. S., Lewis, M., and Horvath, L.: Invasion by extremes: population
spread with variation in dispersal and reproduction, Am. Nat., 157,
537–554, https://doi.org/10.1086/319934, 2001.
Clark, J. S., Lewis, M., McLachlan, J. S., and HilleRisLambers, J. Estimating population spread: what can we forecast and how well?, Ecology, 84, 1979–1988, https://doi.org/10.1890/01-0618, 2003.
Collingham, Y. C. and Huntley, B. Impacts of habitat fragmentation and
patch size upon migration rates, Ecol. Appl., 10, 131–144,
https://doi.org/10.1890/1051-0761(2000)010[0131:IOHFAP]2.0.CO;2, 2000.
Corlett, R. T. and Westcott, D. A.: Will plant movements keep up with
climate change?, Trends Ecol. Evol., 28, 482–488,
https://doi.org/10.1016/j.tree.2013.04.003, 2013.
Cousens, R. D., Hughes, B. D., and Mesgaran, M. B.: Why we do not expect dispersal probability density functions based on a single mechanism to fit real seed shadows, J. Ecol., 106, 903–906, https://doi.org/10.1111/1365-2745.12891, 2018.
Daniell, J. R. G.: A dendrochronological study of subfossil Pinus sylvestris L. stumps from
peat deposits at Badanloch in the far north of Scotland, Doctoral
dissertation, Durham University, http://etheses.dur.ac.uk/6140/ (last access: 16 February 2022), 1992.
Daniell, J. R. G.: The Late-Holocene palaeoecology of Scots pine (Pinus sylvestris L.) in
north-west Scotland, Doctoral dissertation, Durham University,
http://etheses.dur.ac.uk/1219/ (last access: 16 February 2022), 1997.
De Meester, L., Stoks, R., and Brans, K. I. Genetic adaptation as a
biological buffer against climate change: Potential and limitations, Integr.
Zool., 13, 372–391, https://doi.org/10.1111/1749-4877.12298, 2018.
Downing, D. J., Gardner, R. H., and Hoffman, F. O.: An examination of
response-surface methodologies for uncertainty analysis in assessment
models, Technometrics, 27, 151–163,
https://doi.org/10.1080/00401706.1985.10488032, 1985.
Dullinger, S., Dirnböck, T., and Grabherr, G.: Modelling climate
change-driven treeline shifts: relative effects of temperature increase,
dispersal and invasibility, J. Ecol., 92, 241–252,
https://doi.org/10.1111/j.0022-0477.2004.00872.x, 2004.
Dullinger, S., Dendoncker, N., Gattringer, A., Leitner, M., Mang, T., Moser, D., Mücher, C. A., Plutzar, C., Mark Rounsevell, M., Willner, W., Zimmermann, N. E., and Hülber, K.: Modelling the effect of habitat fragmentation on
climate-driven migration of European forest understorey plants, Divers.
Distrib., 21, 1375–1387, https://doi.org/10.1111/ddi.12370, 2015.
Feurdean, A., Bhagwat, S. A., Willis, K. J., Birks, H. J. B., Lischke, H.,
and Hickler, T.: Tree migration-rates: narrowing the gap between inferred
post-glacial rates and projected rates, PLoS One, 8, e71797,
https://doi.org/10.1371/journal.pone.0071797, 2013.
Gear, A. J. and Huntley, B. Rapid changes in the range limits of Scots pine
4000 years ago, Science, 251, 544–547,
https://doi.org/10.1126/science.251.4993.544, 1991.
Giannakos, P.: Frugivory and seed dispersal by carnivores in the Rhodopi
mountains of northern Greece, Doctoral dissertation, Durham University,
http://etheses.dur.ac.uk/4900/ (last access: 16 February 2022), 1997.
Giesecke, T. and Brewer, S.: Notes on the postglacial spread of abundant
European tree taxa, Veg. Hist. Archaeobot., 27, 337–349,
https://doi.org/10.1007/s00334-017-0640-0, 2018.
Giesecke, T., Brewer, S., Finsinger, W., Leydet, M., and Bradshaw, R. H.:
Patterns and dynamics of European vegetation change over the last 15,000
years, J. Biogeogr., 44, 1441–1456, https://doi.org/10.1111/jbi.12974,
2017.
Goto, S., Shimatani, K., Yoshimaru, H., and Takahashi, Y.: Fat-tailed gene
flow in the dioecious canopy tree species Fraxinus mandshurica var. japonica
revealed by microsatellites, Mol. Ecol., 15, 2985–2996,
https://doi.org/10.1111/j.1365-294X.2006.02976.x, 2006.
Guo, F., Lenoir, J., and Bonebrake, T. C.: Land-use change interacts with
climate to determine elevational species redistribution, Nat. Commun., 9,
1–7, https://doi.org/10.1038/s41467-018-03786-9, 2018.
Hamby, D. M.: A comparison of sensitivity analysis techniques, Health Phys.,
68, 195–204, 1995.
Higgins, S. I., Clark, J. S., Nathan, R., Hovestadt, T., Schurr, F.,
Fragoso, J. M. V., Aguiar, M. R., Ribbens, E., and Lavorel, S.: Forecasting
plant migration rates: managing uncertainty for risk assessment, J. Ecol.,
91, 341–347, https://doi.org/10.1046/j.1365-2745.2003.00781.x, 2003a.
Higgins, S. I., Nathan, R., and Cain, M. L.: Are long-distance dispersal
events in plants usually caused by nonstandard means of dispersal?, Ecology,
84, 1945–1956, https://doi.org/10.1890/01-0616, 2003b.
Huntley, B.: Extreme temporal interpolation of sparse data is not a
sufficient basis to substantiate a claim to have uncovered Pleistocene
forest microrefugia, New Phytol., 204, 447–449,
https://doi.org/10.1111/nph.12941, 2014.
Huntley, B. and Birks, H. J. B. (Eds.): Atlas of past and present pollen
maps for Europe: 0–13,000 years ago, Cambridge University Press, https://doi.org/10.1017/S0079497X00007660, 1983.
Huntley, B., Daniell, J. R., and Allen, J. R. Scottish vegetation history:
the Highlands, Bot. J. Scot., 49, 163–175,
https://doi.org/10.1080/03746609708684864, 1997.
Huntley, B., Allen, J. R., Collingham, Y. C., Hickler, T., Lister, A. M.,
Singarayer, J., Stuart, A. J., Sykes, M. T., and Valdes, P. J.: Millennial
climatic fluctuations are key to the structure of last glacial ecosystems,
PLoS One, 8, e61963, https://doi.org/10.1371/journal.pone.0061963, 2013.
Kattge, J., Bönisch, G., Díaz, S., Lavorel, S., Prentice, I. C.,
Leadley, P., et al.: TRY plant trait database–enhanced coverage
and open access, Glob Change Biol., 26, 119–188,
https://doi.org/10.1111/gcb.14904, 2020.
Klein, E. K., Lavigne, C., Picault, H., Renard, M., and Gouyon, P. H.:
Pollen dispersal of oilseed rape: estimation of the dispersal function and
effects of field dimension, J. Appl. Ecol., 43, 141–151,
https://doi.org/10.1111/j.1365-2664.2005.01108.x, 2006.
Lehsten, V., Mischurow, M., Lindström, E., Lehsten, D., and Lischke, H.: LPJ-GM 1.0: simulating migration efficiently in a dynamic vegetation model, Geosci. Model Dev., 12, 893–908, https://doi.org/10.5194/gmd-12-893-2019, 2019.
Lenoir, J., Bertrand, R., Comte, L., Bourgeaud, L., Hattab, T., Murienne,
J., and Grenouillet, G.: Species better track climate warming in the oceans
than on land, Nature Ecol. Evol., 4, 1044–1059,
https://doi.org/10.1038/s41559-020-1198-2, 2020.
Lischke, H. and Löffler, T. J.: Intra-specific density dependence is
required to maintain species diversity in spatio-temporal forest simulations
with reproduction, Ecol. Model., 198, 341–361,
https://doi.org/10.1016/j.ecolmodel.2006.05.005, 2006.
Lischke, H., Zimmermann, N. E., Bolliger, J., Rickebusch, S., and
Löffler, T. J.: TreeMig: a forest-landscape model for simulating
spatio-temporal patterns from stand to landscape scale, Ecol. Model., 199,
409–420, https://doi.org/10.1016/j.ecolmodel.2005.11.046, 2006.
Lovas-Kiss, Á., Vizi, B., Vincze, O., Molnár V., A., and Green, A.
J.: Endozoochory of aquatic ferns and angiosperms by mallards in Central
Europe, J. Ecol., 106, 1714–1723, https://doi.org/10.1111/1365-2745.12913,
2018.
Lustenhouwer, N., Moran, E. V., and Levine, J. M.: Trait correlations
equalize spread velocity across plant life histories, Global Ecol.
Biogeogr., 26, 1398–1407, https://doi.org/10.1111/geb.12662, 2017.
Loehle, C.: Challenges of ecological complexity, Ecol. Complex., 1, 3–6,
https://doi.org/10.1016/j.ecocom.2003.09.001, 2004.
Manel, S., Gaggiotti, O. E., and Waples, R. S.: Assignment methods: matching
biological questions with appropriate techniques, Trends Ecol. Evol., 20,
136–142, https://doi.org/10.1016/j.tree.2004.12.004, 2005.
MacDonald, G. M.: Fossil pollen analysis and the reconstruction of plant
invasions, Adv. Ecol. Res., 24, 67–110,
https://doi.org/10.1016/S0065-2504(08)60041-0, 1993.
McKenzie, P. F., Duveneck, M. J., Morreale, L. L., and Thompson, J. R.:
Local and global parameter sensitivity within an ecophysiologically based
forest landscape model, Environ. Modell. Softw., 117, 1–13,
https://doi.org/10.1016/j.envsoft.2019.03.002, 2019.
Mersmann, O., Bischl, B., Trautmann, H., Preuss, M., Weihs, C., and Rudolph,
G.: Exploratory landscape analysis, in: Proceedings of the 13th annual
conference on Genetic and evolutionary computation, GECCO '11: Genetic and Evolutionary Computation Conference, Dublin, Ireland, 12–16 July 2011, 829–836,
https://doi.org/10.1145/2001576.2001690, July, 2011.
Mladenoff, D. J.: LANDIS and forest landscape models, Ecol. Modell., 180,
7–19, https://doi.org/10.1016/j.ecolmodel.2004.03.016, 2004.
Morales, P., Hickler, T., Rowell, D. P., Smith, B., and Sykes, M. T.:
Changes in European ecosystem productivity and carbon balance driven by
regional climate model output, Glob. Change Biol., 13, 108–122,
https://doi.org/10.1111/j.1365-2486.2006.01289.x, 2007.
Moran, E. V. and Clark, J. S.: Estimating seed and pollen movement in a
monoecious plant: a hierarchical Bayesian approach integrating genetic and
ecological data, Mol. Ecol., 20, 1248–1262,
https://doi.org/10.1111/j.1365-294X.2011.05019.x, 2011.
Nathan, R. and Katul, G. G.: Foliage shedding in deciduous forests lifts up
long-distance seed dispersal by wind, P. Natl. Aacad. Sci. USA, 102, 8251–8256,
https://doi.org/10.1073/pnas.0503048102, 2005.
Nathan, R., Schurr, F. M., Spiegel, O., Steinitz, O., Trakhtenbrot, A., and Tsoar, A.: Mechanisms of long-distance seed dispersal, Trends Ecol. Evol., 23, 638–647, https://doi.org/10.1016/j.tree.2008.08.003, 2008.
Nathan, R., Klein, E. K., Robledo-Arnuncio, J. J., and Revilla, E.:
Dispersal kernels: review, in: Dispersal Ecology and Evolution, Oxford Scholarship Online, 187–210, https://doi.org/10.1093/acprof:oso/9780199608898.001.0001,
2012.
Nobis, M. P. and Normand, S.: KISSMig – a simple model for R to account
for limited migration in analyses of species distributions, Ecography, 37,
1282–1287, https://doi.org/10.1111/ecog.00930, 2014.
Normand, S., Ricklefs, R. E., Skov, F., Bladt, J., Tackenberg, O., and
Svenning, J. C.: Postglacial migration supplements climate in determining
plant species ranges in Europe, P. Roy. Soc. B-Biol. Sci., 278, 3644–3653,
https://doi.org/10.1098/rspb.2010.2769, 2011.
Nogués-Bravo, D., Rodríguez-Sánchez, F., Orsini, L., de Boer,
E., Jansson, R., Morlon, H., Fordham, D. A., and Jackson, S. T.: Cracking
the code of biodiversity responses to past climate change, Trends Ecol.
Evol., 33, 765–77, https://doi.org/10.1016/j.tree.2018.07.005, 2018.
Palmé, A. E., Su, Q., Rautenberg, A., Manni, F., and Lascoux, M.:
Postglacial recolonization and cpDNA variation of silver birch, Betula
pendula, Mol. Ecol., 12, 201–212,
https://doi.org/10.1046/j.1365-294X.2003.01724.x, 2003.
Pappas, C., Fatichi, S., Leuzinger, S., Wolf, A., and Burlando, P.:
Sensitivity analysis of a process-based ecosystem model: Pinpointing
parameterization and structural issues, J. Geophys. Res.-Biogeo., 118,
505–528, https://doi.org/10.1002/jgrg.20035, 2013.
Pecl, G. T., Araújo, M. B., Bell, J. D., Blanchard, J., Bonebrake, T.
C., Chen, I. C., Clark, T. D., Colwel, R. K., Danielsen, F., Evengård,
B., Falconi, L., Ferrier, S., Frusher, S., Garcia, R. A., Griffis, R. B.,
Hobday, A. J., Janion-Scheepers, C., Jarzyna, M. A., Jennings, S., Lenoir,
J., Linnetved, H. I., Martin, V. Y., McCormack, P. C., McDonald, J.,
Mitchell, N. J., Mustonen, T., Pandolfi, J. M., Pettorelli, N., Popova, E.,
Robinson, S. A., Scheffers, B. R., Shaw, J. D., Sorte, C. J. B., Strugnell,
J. M., Sunday, J. M., Tuanmu, M.-N., Vergés, A., Villanueva, C.,
Wernberg, T., Wapstra, E., and Williams, S. E.: Biodiversity redistribution
under climate change: Impacts on ecosystems and human well-being, Science,
355, eaai9214, https://doi.org/10.1126/science.aai9214, 2017.
Petit, R. J., Brewer, S., Bordács, S., Burg, K., Cheddadi, R., Coart,
E., Cottrell, J., Csaikl, U. M., van Dam, B., Deans, J. D., Espinel, S.,
Fineschi, S., Finkeldey, R., Glaz, I., Goicoechea, P. G., Jensen, J. S.,
König, A. O., Lowe, A. J., Madsen, S. F., Mátyás, G., Munro, R.
C., Popescu, F., Slade, D., Tabbener J., de Vries, S. G. M., Ziegenhagen,
B., de Beaulieu J.-L., and Kremer, A.: Identification of refugia and
post-glacial colonisation routes of European white oaks based on chloroplast
DNA and fossil pollen evidence, Forest Ecol. Manag., 156, 49–74,
https://doi.org/10.1016/S0378-1127(01)00634-X, 2002.
Petter, G., Mairota, P., Albrich, K., Bebi, P., Brůna, J., Bugmann, H.,
Haffenden, A., Scheller, R. M., Schmatz, D. R., Seidl, R., Speich, M.,
Vacchiano, G., and Lischke, H.: How robust are future projections of forest
landscape dynamics? Insights from a systematic comparison of four forest
landscape models, Environ. Modell. Softw., 134, 104844,
https://doi.org/10.1016/j.envsoft.2020.104844, 2020.
Powell, J. A. and Zimmermann, N. E.: Multiscale analysis of active seed dispersal contributes to resolving Reid's paradox, Ecology, 85, 490–506, https://doi.org/10.1890/02-0535, 2004.
Reid, C.: The origin of the British flora, Dulau, London, https://doi.org/10.1038/062268a0, 1899.
Rogers, H. S., Beckman, N. G., Hartig, F., Johnson, J. S., Pufal, G., Shea,
K., Zurell, D., Bullock, J. M., Cantrell, R. S., Loiselle, B., Pejchar, L.,
Razafindratsima, O. H., Sandor, M. E., Schupp, E. W., Strickland, W. C., and
Zambrano, J.: The total dispersal kernel: a review and future directions,
AoB Plants, 11, https://doi.org/10.1093/aobpla/plz042, 2019.
Royal Botanic Gardens Kew: Seed Information Database (SID), Version 7.0.,
http://data.kew.org/sid/, last access: 19 December 2019.
Saltelli, A., Tarantola, S., and Campolongo, F.: Sensitivity analysis as an
ingredient of modeling, Stat. Sci., 15, 377–395,
https://doi.org/10.1214/ss/1009213004, 2000.
Saltré, F., Duputié, A., Gaucherel, C., and Chuine, I.: How climate,
migration ability and habitat fragmentation affect the projected future
distribution of European beech, Glob. Change Biol., 21, 897–910,
https://doi.org/10.1111/gcb.12771, 2015.
Scherrer, D., Vitasse, Y., Guisan, A., Wohlgemuth, T., and Lischke, H.:
Competition and demography rather than dispersal limitation slow down upward
shifts of trees' upper elevation limits in the Alps, J. Ecol., 108,
2416–2430, https://doi.org/10.1111/1365-2745.13451, 2020.
Schumacher, S., Bugmann, H., and Mladenoff, D. J.: Improving the formulation
of tree growth and succession in a spatially explicit landscape model, Ecol.
Modell., 180, 175–194, https://doi.org/10.1016/j.ecolmodel.2003.12.055,
2004.
Schurr, F. M., Spiegel, O., Steinitz, O., Trakhtenbrot, A., Tsoar, A., and Nathan, R.: Long-distance seed dispersal, in: Annual Plant Reviews, Fruit Development and Seed Dispersal, edited by: Østergaard, L., Blackwell Publishing Ltd., 38, 204–237, https://doi.org/10.1002/9781444314557.ch6, 2009.
Seidl, R., Rammer, W., Scheller, R. M., and Spies, T. A.: An
individual-based process model to simulate landscape-scale forest ecosystem
dynamics, Ecol. Modell., 231, 87–100,
https://doi.org/10.1016/j.ecolmodel.2012.02.015, 2012.
Shifley, S. R., He, H. S., Lischke, H., Wang, W. J., Jin, W., Gustafson, E.
J., Thompson, J. R., Thompson, F. R., Dijak,W. D., and Yang, J.: The past
and future of modeling forest dynamics: from growth and yield curves to
forest landscape models, Landscape Ecol., 32, 1307–1325,
https://doi.org/10.1007/s10980-017-0540-9, 2017.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol.
Biogeogr., 10, 621–637, 2001.
Smith, B., Arneth, A., Arvanitis, T., Bondeau, A., Chaudhary, N., Cramer, W., Eliasson, P., Gerten, D., Hickler, T., Holmér, J., Kaplan, J., Knorr, W., Lehsten, D., Lehsten, V., Lindeskog, M., Lucht, W., Miller, P., Mishurov, M., Olin, S., Poska, A., Pugh, T., Prentice, C., Rammig, A., Schaphoff, S., Schurgers, G., Siltberg, J., Sitch, S., Sykes, M., Thonicke, K., Venevsky, S., Wania, R., Wårlind, D., Wolf, A., Wramneby, A., and Zaehle, S.: LPJ-GUESS Education 3.0, https://web.nateko.lu.se/lpj-guess/, last access: 28 October 2021.
Snell, R. S., Huth, A., Nabel, J. E., Bocedi, G., Travis, J. M., Gravel, D.,
Bugmann, H., Gutiérrez, A. G., Hickler, T., Higgins, S. I., Reineking,
B., Scherstjanoi, M., Zurbriggen, N., and Lischke, H.: Using dynamic
vegetation models to simulate plant range shifts, Ecography, 37, 1184–1197,
https://doi.org/10.1111/ecog.00580, 2014.
Soons, M. B., Heil, G. W., Nathan, R., and Katul, G. G.: Determinants of
long-distance seed dispersal by wind in grasslands, Ecology, 85, 3056–3068,
https://doi.org/10.1890/03-0522, 2004.
Snowling, S. D. and Kramer, J. R.: Evaluating modelling uncertainty for
model selection, Ecol. Modell., 138, 17–30,
https://doi.org/10.1016/S0304-3800(00)00390-2, 2001.
Stewart, J. R. and Lister, A. M.: Cryptic northern refugia and the origins
of the modern biota, Trends Ecol. Evol., 16, 608–613,
https://doi.org/10.1016/S0169-5347(01)02338-2, 2001.
Stork, J., Eiben, A. E., and Bartz-Beielstein, T.: A new taxonomy of global
optimization algorithms, Nat. Comput., 21, 219–242,
https://doi.org/10.1007/s11047-020-09820-4, 2020.
Svenning, J. C. and Sandel, B.: Disequilibrium vegetation dynamics under
future climate change, Am. J. Bot., 100, 1266–1286,
https://doi.org/10.3732/ajb.1200469, 2013.
Tamme, R., Götzenberger, L., Zobel, M., Bullock, J. M., Hooftman, D. A.,
Kaasik, A., and Pärtel, M.: Predicting species' maximum dispersal
distances from simple plant traits, Ecology, 95, 505–513,
https://doi.org/10.1890/13-1000.1, 2014.
Thompson, P. L. and Fronhofer, E. A.: The conflict between adaptation and
dispersal for maintaining biodiversity in changing environments, P. Natl. Aacad. Sci. USA, 116,
21061–21067, https://doi.org/10.1073/pnas.1911796116, 2019.
Tomiolo, S. and Ward, D.: Species migrations and range shifts: A synthesis
of causes and consequences, Perspect. Plant Ecol., 33, 62–77,
https://doi.org/10.1016/j.ppees.2018.06.001, 2018.
Tzedakis, P. C., Emerson, B. C., and Hewitt, G. M.: Cryptic or mystic?
Glacial tree refugia in northern Europe, Trends Ecol. Evol., 28, 696–704,
https://doi.org/10.1016/j.tree.2013.09.001, 2013.
Vittoz, P. and Engler, R.: Seed dispersal distances: a typology based on
dispersal modes and plant traits, Bot. Helv., 117, 109–124,
https://doi.org/10.1007/s00035-007-0797-8, 2007.
Wilkinson, D. M.: Plant colonization: are wind dispersed seeds really
dispersed by birds at larger spatial and temporal scales?, J. Biogeogr., 24,
61–65, https://doi.org/10.1111/j.1365-2699.1997.tb00050.x, 1997.
Wilson, G. A. and Rannala, B.: Bayesian inference of recent migration rates
using multilocus genotypes, Genetics, 163, 1177–1191,
https://doi.org/10.1093/genetics/163.3.1177, 2003.
Wramneby, A., Smith, B., Zaehle, S., and Sykes, M. T.: Parameter
uncertainties in the modelling of vegetation dynamics–effects on tree
community structure and ecosystem functioning in European forest biomes,
Ecol. Model., 216, 277–290,
https://doi.org/10.1016/j.ecolmodel.2008.04.013, 2008.
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter
uncertainties on the modeling of terrestrial biosphere dynamics, Global
Biogeochem. Cy., 19, https://doi.org/10.1029/2004GB002395, 2005.
Zani, D.: Input climate and landscape data to “Tree migration in the dynamic, global vegetation model LPJ-GM 1.1”, DatGURU [data set], https://doi.org/10.18161/20211127, 2021.
Zani, D. and Lehsten, V.: LPJ-GMINT, Dynamic vegetation model (LPJ-Guess) with migration (M) and interacting non-tree species (INT) modules, GitHub [code], https://github.com/zanid90/LPJ-GMINT (last access: 1 November 2021), 2022.
Short summary
The prediction of species migration under rapid climate change remains uncertain. In this paper, we evaluate the importance of the mechanisms underlying plant migration and increase the performance in the dynamic global vegetation model LPJ-GM 1.0. The improved model will allow us to understand past vegetation dynamics and predict the future redistribution of species in a context of global change.
The prediction of species migration under rapid climate change remains uncertain. In this paper,...