Articles | Volume 21, issue 11
https://doi.org/10.5194/bg-21-2839-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-2839-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jun 2024
Research article |
| 13 Jun 2024
| 13 Jun 2024
Mapping the future afforestation distribution of China constrained by a national afforestation plan and climate change
Shuaifeng Song
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, People's Republic of China
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, People's Republic of China
Xuezhen Zhang
CORRESPONDING AUTHOR
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, People's Republic of China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, People's Republic of China
Related authors
No articles found.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
Short summary
Short summary
Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Jing Fang, Xing Li, Jingfeng Xiao, Xiaodong Yan, Bolun Li, and Feng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-452, https://doi.org/10.5194/essd-2021-452, 2022
Revised manuscript not accepted
Short summary
Short summary
The dataset provided the vegetation photosynthetic phenology instead of traditional phenology to represent plant seasonal activities. This dataset had the latest period (2001–2020) and a fine spatial resolution (0.05 degree). Our phenology metrics revealed the spatial-temporal patterns of the multiple growing seasons in the Northern Hemisphere. The dataset will facilitate various research such as developing models, evaluating phenology shifts, and monitoring climate change worldwide.
Zhixin Hao, Maowei Wu, Jingyun Zheng, Jiewei Chen, Xuezhen Zhang, and Shiwei Luo
Clim. Past, 16, 101–116, https://doi.org/10.5194/cp-16-101-2020, https://doi.org/10.5194/cp-16-101-2020, 2020
Short summary
Short summary
Using reconstructed extreme drought/flood chronologies and grain harvest series derived from historical documents, it is found that the frequency of reporting of extreme droughts in any subregion of eastern China was significantly associated with lower reconstructed harvests during 801–1910. The association was weak during the warm epoch of 920–1300 but strong during the cold epoch of 1310–1880, which indicates that a warm climate might weaken the impact of extreme drought on poor harvests.
Jingyun Zheng, Yingzhuo Yu, Xuezhen Zhang, and Zhixin Hao
Clim. Past, 14, 1135–1145, https://doi.org/10.5194/cp-14-1135-2018, https://doi.org/10.5194/cp-14-1135-2018, 2018
Short summary
Short summary
We investigated the decadal variations of extreme droughts and floods in North China using a 17-site seasonal precipitation reconstruction from a unique historical archive. Then, the link of extreme droughts and floods with ENSO episodes and large volcanic eruptions was discussed. This study helps us understand whether the recent extreme events observed by instruments exceed the natural variability at a regional scale, which may be useful for adaptation to extremes and disasters in the future.
Related subject area
Earth System Science/Response to Global Change: Climate Change
Southern Ocean phytoplankton under climate change: a shifting balance of bottom-up and top-down control
Coherency and time lag analyses between MODIS vegetation indices and climate across forests and grasslands in the European temperate zone
Direct foliar phosphorus uptake from wildfire ash
The effect of forest cover changes on the regional climate conditions in Europe during the period 1986–2015
Carbon cycle feedbacks in an idealized simulation and a scenario simulation of negative emissions in CMIP6 Earth system models
Spatiotemporal heterogeneity in the increase in ocean acidity extremes in the northeastern Pacific
The ocean's biological and preformed carbon pumps in future steady-state climate scenarios
Anthropogenic climate change drives non-stationary phytoplankton internal variability
The response of wildfire regimes to Last Glacial Maximum carbon dioxide and climate
The Southern Ocean as the freight train of the global climate under zero-emission scenarios with ACCESS-ESM1.5
Simulated responses of soil carbon to climate change in CMIP6 Earth system models: the role of false priming
Alkalinity biases in CMIP6 Earth system models and implications for simulated CO2 drawdown via artificial alkalinity enhancement
Experiments of the efficacy of tree ring blue intensity as a climate proxy in central and western China
Burned area and carbon emissions across northwestern boreal North America from 2001–2019
Quantifying land carbon cycle feedbacks under negative CO2 emissions
The potential of an increased deciduous forest fraction to mitigate the effects of heat extremes in Europe
Ideas and perspectives: Alleviation of functional limitations by soil organisms is key to climate feedbacks from arctic soils
A comparison of the climate and carbon cycle effects of carbon removal by afforestation and an equivalent reduction in fossil fuel emissions
Stability of alkalinity in ocean alkalinity enhancement (OAE) approaches – consequences for durability of CO2 storage
Ideas and perspectives: Land–ocean connectivity through groundwater
Bioclimatic change as a function of global warming from CMIP6 climate projections
Reconciling different approaches to quantifying land surface temperature impacts of afforestation using satellite observations
Drivers of intermodel uncertainty in land carbon sink projections
Reviews and syntheses: A framework to observe, understand and project ecosystem response to environmental change in the East Antarctic Southern Ocean
Acidification impacts and acclimation potential of Caribbean benthic foraminifera assemblages in naturally discharging low-pH water
Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI)
Evaluation of soil carbon simulation in CMIP6 Earth system models
Diazotrophy as a key driver of the response of marine net primary productivity to climate change
Impact of negative and positive CO2 emissions on global warming metrics using an ensemble of Earth system model simulations
Acidification, deoxygenation, and nutrient and biomass declines in a warming Mediterranean Sea
Ocean alkalinity enhancement – avoiding runaway CaCO3 precipitation during quick and hydrated lime dissolution
Assessment of the impacts of biological nitrogen fixation structural uncertainty in CMIP6 earth system models
Soil carbon loss in warmed subarctic grasslands is rapid and restricted to topsoil
The European forest carbon budget under future climate conditions and current management practices
The influence of mesoscale climate drivers on hypoxia in a fjord-like deep coastal inlet and its potential implications regarding climate change: examining a decade of water quality data
Contrasting responses of phytoplankton productivity between coastal and offshore surface waters in the Taiwan Strait and the South China Sea to short-term seawater acidification
Modeling interactions between tides, storm surges, and river discharges in the Kapuas River delta
The application of dendrometers to alpine dwarf shrubs – a case study to investigate stem growth responses to environmental conditions
Climate, land cover and topography: essential ingredients in predicting wetland permanence
Not all biodiversity rich spots are climate refugia
Evaluating the dendroclimatological potential of blue intensity on multiple conifer species from Tasmania and New Zealand
Anthropogenic CO2-mediated freshwater acidification limits survival, calcification, metabolism, and behaviour in stress-tolerant freshwater crustaceans
Quantifying the role of moss in terrestrial ecosystem carbon dynamics in northern high latitudes
On the influence of erect shrubs on the irradiance profile in snow
Tolerance of tropical marine microphytobenthos exposed to elevated irradiance and temperature
Persistent impacts of the 2018 drought on forest disturbance regimes in Europe
Reviews and syntheses: Arctic fire regimes and emissions in the 21st century
Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO2
Effects of elevated CO2 and extreme climatic events on forage quality and in vitro rumen fermentation in permanent grassland
Cushion bog plant community responses to passive warming in southern Patagonia
Tianfei Xue, Jens Terhaar, A. E. Friederike Prowe, Thomas L. Frölicher, Andreas Oschlies, and Ivy Frenger
Biogeosciences, 21, 2473–2491, https://doi.org/10.5194/bg-21-2473-2024, https://doi.org/10.5194/bg-21-2473-2024, 2024
Short summary
Short summary
Phytoplankton play a crucial role in marine ecosystems. However, climate change's impact on phytoplankton biomass remains uncertain, particularly in the Southern Ocean. In this region, phytoplankton biomass within the water column is likely to remain stable in response to climate change, as supported by models. This stability arises from a shallower mixed layer, favoring phytoplankton growth but also increasing zooplankton grazing due to phytoplankton concentration near the surface.
Kinga Kulesza and Agata Hościło
Biogeosciences, 21, 2509–2527, https://doi.org/10.5194/bg-21-2509-2024, https://doi.org/10.5194/bg-21-2509-2024, 2024
Short summary
Short summary
We present coherence and time lags in spectral response of three vegetation types in the European temperate zone to the influencing meteorological factors and teleconnection indices for the period 2002–2022. Vegetation condition in broadleaved forest, coniferous forest and pastures was measured with MODIS NDVI and EVI, and the coherence between NDVI and EVI and meteorological elements was described using the methods of wavelet coherence and Pearson’s linear correlation with time lag.
Anton Lokshin, Daniel Palchan, and Avner Gross
Biogeosciences, 21, 2355–2365, https://doi.org/10.5194/bg-21-2355-2024, https://doi.org/10.5194/bg-21-2355-2024, 2024
Short summary
Short summary
Ash particles from wildfires are rich in phosphorus (P), a crucial nutrient that constitutes a limiting factor in 43 % of the world's land ecosystems. We hypothesize that wildfire ash could directly contribute to plant nutrition. We find that fire ash application boosts the growth of plants, but the only way plants can uptake P from fire ash is through the foliar uptake pathway and not through the roots. The fertilization impact of fire ash was also maintained under elevated levels of CO2.
Marcus Breil, Vanessa K. M. Schneider, and Joaquim G. Pinto
Biogeosciences, 21, 811–824, https://doi.org/10.5194/bg-21-811-2024, https://doi.org/10.5194/bg-21-811-2024, 2024
Short summary
Short summary
The general impact of afforestation on the regional climate conditions in Europe during the period 1986–2015 is investigated. For this purpose, a regional climate model simulation is performed, in which afforestation during this period is considered, and results are compared to a simulation in which this is not the case. Results show that afforestation had discernible impacts on the climate change signal in Europe, which may have mitigated the local warming trend, especially in summer in Europe.
Ali Asaadi, Jörg Schwinger, Hanna Lee, Jerry Tjiputra, Vivek Arora, Roland Séférian, Spencer Liddicoat, Tomohiro Hajima, Yeray Santana-Falcón, and Chris D. Jones
Biogeosciences, 21, 411–435, https://doi.org/10.5194/bg-21-411-2024, https://doi.org/10.5194/bg-21-411-2024, 2024
Short summary
Short summary
Carbon cycle feedback metrics are employed to assess phases of positive and negative CO2 emissions. When emissions become negative, we find that the model disagreement in feedback metrics increases more strongly than expected from the assumption that the uncertainties accumulate linearly with time. The geographical patterns of such metrics over land highlight that differences in response between tropical/subtropical and temperate/boreal ecosystems are a major source of model disagreement.
Flora Desmet, Matthias Münnich, and Nicolas Gruber
Biogeosciences, 20, 5151–5175, https://doi.org/10.5194/bg-20-5151-2023, https://doi.org/10.5194/bg-20-5151-2023, 2023
Short summary
Short summary
Ocean acidity extremes in the upper 250 m depth of the northeastern Pacific rapidly increase with atmospheric CO2 rise, which is worrisome for marine organisms that rapidly experience pH levels outside their local environmental conditions. Presented research shows the spatiotemporal heterogeneity in this increase between regions and depths. In particular, the subsurface increase is substantially slowed down by the presence of mesoscale eddies, often not resolved in Earth system models.
Benoît Pasquier, Mark Holzer, and Matthew A. Chamberlain
EGUsphere, https://doi.org/10.5194/egusphere-2023-2525, https://doi.org/10.5194/egusphere-2023-2525, 2023
Short summary
Short summary
How will the future ocean sequester carbon? We find that for an ocean perpetually warmer with slower circulation, biological productivity declines despite warming-stimulated growth because of lower nutrient supply from depth. This throttles the biological carbon pump, which still sequesters more carbon because it takes longer to return to the surface. The deep ocean is isolated from the surface allowing more carbon from the atmosphere to pass through the ocean without contributing to biology.
Geneviève W. Elsworth, Nicole S. Lovenduski, Kristen M. Krumhardt, Thomas M. Marchitto, and Sarah Schlunegger
Biogeosciences, 20, 4477–4490, https://doi.org/10.5194/bg-20-4477-2023, https://doi.org/10.5194/bg-20-4477-2023, 2023
Short summary
Short summary
Anthropogenic climate change will influence marine phytoplankton over the coming century. Here, we quantify the influence of anthropogenic climate change on marine phytoplankton internal variability using an Earth system model ensemble and identify a decline in global phytoplankton biomass variance with warming. Our results suggest that climate mitigation efforts that account for marine phytoplankton changes should also consider changes in phytoplankton variance driven by anthropogenic warming.
Olivia Haas, Iain Colin Prentice, and Sandy P. Harrison
Biogeosciences, 20, 3981–3995, https://doi.org/10.5194/bg-20-3981-2023, https://doi.org/10.5194/bg-20-3981-2023, 2023
Short summary
Short summary
We quantify the impact of CO2 and climate on global patterns of burnt area, fire size, and intensity under Last Glacial Maximum (LGM) conditions using three climate scenarios. Climate change alone did not produce the observed LGM reduction in burnt area, but low CO2 did through reducing vegetation productivity. Fire intensity was sensitive to CO2 but strongly affected by changes in atmospheric dryness. Low CO2 caused smaller fires; climate had the opposite effect except in the driest scenario.
Matthew A. Chamberlain, Tilo Ziehn, and Rachel M. Law
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-146, https://doi.org/10.5194/bg-2023-146, 2023
Revised manuscript accepted for BG
Short summary
Short summary
This manuscript explores the climate processes that drive increasing global average temperatures in Zero-Emission Commitment (ZEC) simulations despite decreasing atmospheric CO2. The ACCESS-ESM1.5 shows the Southern Ocean to continue to warm locally in all ZEC simulations. In ZEC simulations that start after the emission of more than 1000 Pg of carbon, the influence of the Southern Ocean increases the global temperature.
Rebecca M. Varney, Sarah E. Chadburn, Eleanor J. Burke, Simon Jones, Andy J. Wiltshire, and Peter M. Cox
Biogeosciences, 20, 3767–3790, https://doi.org/10.5194/bg-20-3767-2023, https://doi.org/10.5194/bg-20-3767-2023, 2023
Short summary
Short summary
This study evaluates soil carbon projections during the 21st century in CMIP6 Earth system models. In general, we find a reduced spread of changes in global soil carbon in CMIP6 compared to the previous CMIP5 generation. The reduced CMIP6 spread arises from an emergent relationship between soil carbon changes due to change in plant productivity and soil carbon changes due to changes in turnover time. We show that this relationship is consistent with false priming under transient climate change.
Claudia Hinrichs, Peter Köhler, Christoph Völker, and Judith Hauck
Biogeosciences, 20, 3717–3735, https://doi.org/10.5194/bg-20-3717-2023, https://doi.org/10.5194/bg-20-3717-2023, 2023
Short summary
Short summary
This study evaluated the alkalinity distribution in 14 climate models and found that most models underestimate alkalinity at the surface and overestimate it in the deeper ocean. It highlights the need for better understanding and quantification of processes driving alkalinity distribution and calcium carbonate dissolution and the importance of accounting for biases in model results when evaluating potential ocean alkalinity enhancement experiments.
Yonghong Zheng, Huanfeng Shen, Rory Abernethy, and Rob Wilson
Biogeosciences, 20, 3481–3490, https://doi.org/10.5194/bg-20-3481-2023, https://doi.org/10.5194/bg-20-3481-2023, 2023
Short summary
Short summary
Investigations in central and western China show that tree ring inverted latewood intensity expresses a strong positive relationship with growing-season temperatures, indicating exciting potential for regions south of 30° N that are traditionally not targeted for temperature reconstructions. Earlywood BI also shows good potential to reconstruct hydroclimate parameters in some humid areas and will enhance ring-width-based hydroclimate reconstructions in the future.
Stefano Potter, Sol Cooperdock, Sander Veraverbeke, Xanthe Walker, Michelle C. Mack, Scott J. Goetz, Jennifer Baltzer, Laura Bourgeau-Chavez, Arden Burrell, Catherine Dieleman, Nancy French, Stijn Hantson, Elizabeth E. Hoy, Liza Jenkins, Jill F. Johnstone, Evan S. Kane, Susan M. Natali, James T. Randerson, Merritt R. Turetsky, Ellen Whitman, Elizabeth Wiggins, and Brendan M. Rogers
Biogeosciences, 20, 2785–2804, https://doi.org/10.5194/bg-20-2785-2023, https://doi.org/10.5194/bg-20-2785-2023, 2023
Short summary
Short summary
Here we developed a new burned-area detection algorithm between 2001–2019 across Alaska and Canada at 500 m resolution. We estimate 2.37 Mha burned annually between 2001–2019 over the domain, emitting 79.3 Tg C per year, with a mean combustion rate of 3.13 kg C m−2. We found larger-fire years were generally associated with greater mean combustion. The burned-area and combustion datasets described here can be used for local- to continental-scale applications of boreal fire science.
V. Rachel Chimuka, Claude-Michel Nzotungicimpaye, and Kirsten Zickfeld
Biogeosciences, 20, 2283–2299, https://doi.org/10.5194/bg-20-2283-2023, https://doi.org/10.5194/bg-20-2283-2023, 2023
Short summary
Short summary
We propose a new method to quantify carbon cycle feedbacks under negative CO2 emissions. Our method isolates the lagged carbon cycle response to preceding positive emissions from the response to negative emissions. Our findings suggest that feedback parameters calculated with the novel approach are larger than those calculated with the conventional approach whereby carbon cycle inertia is not corrected for, with implications for the effectiveness of carbon dioxide removal in reducing CO2 levels.
Marcus Breil, Annabell Weber, and Joaquim G. Pinto
Biogeosciences, 20, 2237–2250, https://doi.org/10.5194/bg-20-2237-2023, https://doi.org/10.5194/bg-20-2237-2023, 2023
Short summary
Short summary
A promising strategy for mitigating burdens of heat extremes in Europe is to replace dark coniferous forests with brighter deciduous forests. The consequence of this would be reduced absorption of solar radiation, which should reduce the intensities of heat periods. In this study, we show that deciduous forests have a certain cooling effect on heat period intensities in Europe. However, the magnitude of the temperature reduction is quite small.
Gesche Blume-Werry, Jonatan Klaminder, Eveline J. Krab, and Sylvain Monteux
Biogeosciences, 20, 1979–1990, https://doi.org/10.5194/bg-20-1979-2023, https://doi.org/10.5194/bg-20-1979-2023, 2023
Short summary
Short summary
Northern soils store a lot of carbon. Most research has focused on how this carbon storage is regulated by cold temperatures. However, it is soil organisms, from minute bacteria to large earthworms, that decompose the organic material. Novel soil organisms from further south could increase decomposition rates more than climate change does and lead to carbon losses. We therefore advocate for including soil organisms when predicting the fate of soil functions in warming northern ecosystems.
Koramanghat Unnikrishnan Jayakrishnan and Govindasamy Bala
Biogeosciences, 20, 1863–1877, https://doi.org/10.5194/bg-20-1863-2023, https://doi.org/10.5194/bg-20-1863-2023, 2023
Short summary
Short summary
Afforestation and reducing fossil fuel emissions are two important mitigation strategies to reduce the amount of global warming. Our work shows that reducing fossil fuel emissions is relatively more effective than afforestation for the same amount of carbon removed from the atmosphere. However, understanding of the processes that govern the biophysical effects of afforestation should be improved before considering our results for climate policy.
Jens Hartmann, Niels Suitner, Carl Lim, Julieta Schneider, Laura Marín-Samper, Javier Arístegui, Phil Renforth, Jan Taucher, and Ulf Riebesell
Biogeosciences, 20, 781–802, https://doi.org/10.5194/bg-20-781-2023, https://doi.org/10.5194/bg-20-781-2023, 2023
Short summary
Short summary
CO2 can be stored in the ocean via increasing alkalinity of ocean water. Alkalinity can be created via dissolution of alkaline materials, like limestone or soda. Presented research studies boundaries for increasing alkalinity in seawater. The best way to increase alkalinity was found using an equilibrated solution, for example as produced from reactors. Adding particles for dissolution into seawater on the other hand produces the risk of losing alkalinity and degassing of CO2 to the atmosphere.
Damian L. Arévalo-Martínez, Amir Haroon, Hermann W. Bange, Ercan Erkul, Marion Jegen, Nils Moosdorf, Jens Schneider von Deimling, Christian Berndt, Michael Ernst Böttcher, Jasper Hoffmann, Volker Liebetrau, Ulf Mallast, Gudrun Massmann, Aaron Micallef, Holly A. Michael, Hendrik Paasche, Wolfgang Rabbel, Isaac Santos, Jan Scholten, Katrin Schwalenberg, Beata Szymczycha, Ariel T. Thomas, Joonas J. Virtasalo, Hannelore Waska, and Bradley A. Weymer
Biogeosciences, 20, 647–662, https://doi.org/10.5194/bg-20-647-2023, https://doi.org/10.5194/bg-20-647-2023, 2023
Short summary
Short summary
Groundwater flows at the land–ocean transition and the extent of freshened groundwater below the seafloor are increasingly relevant in marine sciences, both because they are a highly uncertain term of biogeochemical budgets and due to the emerging interest in the latter as a resource. Here, we discuss our perspectives on future research directions to better understand land–ocean connectivity through groundwater and its potential responses to natural and human-induced environmental changes.
Morgan Sparey, Peter Cox, and Mark S. Williamson
Biogeosciences, 20, 451–488, https://doi.org/10.5194/bg-20-451-2023, https://doi.org/10.5194/bg-20-451-2023, 2023
Short summary
Short summary
Accurate climate models are vital for mitigating climate change; however, projections often disagree. Using Köppen–Geiger bioclimate classifications we show that CMIP6 climate models agree well on the fraction of global land surface that will change classification per degree of global warming. We find that 13 % of land will change climate per degree of warming from 1 to 3 K; thus, stabilising warming at 1.5 rather than 2 K would save over 7.5 million square kilometres from bioclimatic change.
Huanhuan Wang, Chao Yue, and Sebastiaan Luyssaert
Biogeosciences, 20, 75–92, https://doi.org/10.5194/bg-20-75-2023, https://doi.org/10.5194/bg-20-75-2023, 2023
Short summary
Short summary
This study provided a synthesis of three influential methods to quantify afforestation impact on surface temperature. Results showed that actual effect following afforestation was highly dependent on afforestation fraction. When full afforestation is assumed, the actual effect approaches the potential effect. We provided evidence the afforestation faction is a key factor in reconciling different methods and emphasized that it should be considered for surface cooling impacts in policy evaluation.
Ryan S. Padrón, Lukas Gudmundsson, Laibao Liu, Vincent Humphrey, and Sonia I. Seneviratne
Biogeosciences, 19, 5435–5448, https://doi.org/10.5194/bg-19-5435-2022, https://doi.org/10.5194/bg-19-5435-2022, 2022
Short summary
Short summary
The answer to how much carbon land ecosystems are projected to remove from the atmosphere until 2100 is different for each Earth system model. We find that differences across models are primarily explained by the annual land carbon sink dependence on temperature and soil moisture, followed by the dependence on CO2 air concentration, and by average climate conditions. Our insights on why each model projects a relatively high or low land carbon sink can help to reduce the underlying uncertainty.
Julian Gutt, Stefanie Arndt, David Keith Alan Barnes, Horst Bornemann, Thomas Brey, Olaf Eisen, Hauke Flores, Huw Griffiths, Christian Haas, Stefan Hain, Tore Hattermann, Christoph Held, Mario Hoppema, Enrique Isla, Markus Janout, Céline Le Bohec, Heike Link, Felix Christopher Mark, Sebastien Moreau, Scarlett Trimborn, Ilse van Opzeeland, Hans-Otto Pörtner, Fokje Schaafsma, Katharina Teschke, Sandra Tippenhauer, Anton Van de Putte, Mia Wege, Daniel Zitterbart, and Dieter Piepenburg
Biogeosciences, 19, 5313–5342, https://doi.org/10.5194/bg-19-5313-2022, https://doi.org/10.5194/bg-19-5313-2022, 2022
Short summary
Short summary
Long-term ecological observations are key to assess, understand and predict impacts of environmental change on biotas. We present a multidisciplinary framework for such largely lacking investigations in the East Antarctic Southern Ocean, combined with case studies, experimental and modelling work. As climate change is still minor here but is projected to start soon, the timely implementation of this framework provides the unique opportunity to document its ecological impacts from the very onset.
Daniel François, Adina Paytan, Olga Maria Oliveira de Araújo, Ricardo Tadeu Lopes, and Cátia Fernandes Barbosa
Biogeosciences, 19, 5269–5285, https://doi.org/10.5194/bg-19-5269-2022, https://doi.org/10.5194/bg-19-5269-2022, 2022
Short summary
Short summary
Our analysis revealed that under the two most conservative acidification projections foraminifera assemblages did not display considerable changes. However, a significant decrease in species richness was observed when pH decreases to 7.7 pH units, indicating adverse effects under high-acidification scenarios. A micro-CT analysis revealed that calcified tests of Archaias angulatus were of lower density in low pH, suggesting no acclimation capacity for this species.
Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, https://doi.org/10.5194/bg-19-5107-2022, 2022
Short summary
Short summary
The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
Rebecca M. Varney, Sarah E. Chadburn, Eleanor J. Burke, and Peter M. Cox
Biogeosciences, 19, 4671–4704, https://doi.org/10.5194/bg-19-4671-2022, https://doi.org/10.5194/bg-19-4671-2022, 2022
Short summary
Short summary
Soil carbon is the Earth’s largest terrestrial carbon store, and the response to climate change represents one of the key uncertainties in obtaining accurate global carbon budgets required to successfully militate against climate change. The ability of climate models to simulate present-day soil carbon is therefore vital. This study assesses soil carbon simulation in the latest ensemble of models which allows key areas for future model development to be identified.
Laurent Bopp, Olivier Aumont, Lester Kwiatkowski, Corentin Clerc, Léonard Dupont, Christian Ethé, Thomas Gorgues, Roland Séférian, and Alessandro Tagliabue
Biogeosciences, 19, 4267–4285, https://doi.org/10.5194/bg-19-4267-2022, https://doi.org/10.5194/bg-19-4267-2022, 2022
Short summary
Short summary
The impact of anthropogenic climate change on the biological production of phytoplankton in the ocean is a cause for concern because its evolution could affect the response of marine ecosystems to climate change. Here, we identify biological N fixation and its response to future climate change as a key process in shaping the future evolution of marine phytoplankton production. Our results show that further study of how this nitrogen fixation responds to environmental change is essential.
Negar Vakilifard, Richard G. Williams, Philip B. Holden, Katherine Turner, Neil R. Edwards, and David J. Beerling
Biogeosciences, 19, 4249–4265, https://doi.org/10.5194/bg-19-4249-2022, https://doi.org/10.5194/bg-19-4249-2022, 2022
Short summary
Short summary
To remain within the Paris climate agreement, there is an increasing need to develop and implement carbon capture and sequestration techniques. The global climate benefits of implementing negative emission technologies over the next century are assessed using an Earth system model covering a wide range of plausible climate states. In some model realisations, there is continued warming after emissions cease. This continued warming is avoided if negative emissions are incorporated.
Marco Reale, Gianpiero Cossarini, Paolo Lazzari, Tomas Lovato, Giorgio Bolzon, Simona Masina, Cosimo Solidoro, and Stefano Salon
Biogeosciences, 19, 4035–4065, https://doi.org/10.5194/bg-19-4035-2022, https://doi.org/10.5194/bg-19-4035-2022, 2022
Short summary
Short summary
Future projections under the RCP8.5 and RCP4.5 emission scenarios of the Mediterranean Sea biogeochemistry at the end of the 21st century show different levels of decline in nutrients, oxygen and biomasses and an acidification of the water column. The signal intensity is stronger under RCP8.5 and in the eastern Mediterranean. Under RCP4.5, after the second half of the 21st century, biogeochemical variables show a recovery of the values observed at the beginning of the investigated period.
Charly A. Moras, Lennart T. Bach, Tyler Cyronak, Renaud Joannes-Boyau, and Kai G. Schulz
Biogeosciences, 19, 3537–3557, https://doi.org/10.5194/bg-19-3537-2022, https://doi.org/10.5194/bg-19-3537-2022, 2022
Short summary
Short summary
This research presents the first laboratory results of quick and hydrated lime dissolution in natural seawater. These two minerals are of great interest for ocean alkalinity enhancement, a strategy aiming to decrease atmospheric CO2 concentrations. Following the dissolution of these minerals, we identified several hurdles and presented ways to avoid them or completely negate them. Finally, we proceeded to various simulations in today’s oceans to implement the strategy at its highest potential.
Taraka Davies-Barnard, Sönke Zaehle, and Pierre Friedlingstein
Biogeosciences, 19, 3491–3503, https://doi.org/10.5194/bg-19-3491-2022, https://doi.org/10.5194/bg-19-3491-2022, 2022
Short summary
Short summary
Biological nitrogen fixation is the largest natural input of new nitrogen onto land. Earth system models mainly represent global total terrestrial biological nitrogen fixation within observational uncertainties but overestimate tropical fixation. The model range of increase in biological nitrogen fixation in the SSP3-7.0 scenario is 3 % to 87 %. While biological nitrogen fixation is a key source of new nitrogen, its predictive power for net primary productivity in models is limited.
Niel Verbrigghe, Niki I. W. Leblans, Bjarni D. Sigurdsson, Sara Vicca, Chao Fang, Lucia Fuchslueger, Jennifer L. Soong, James T. Weedon, Christopher Poeplau, Cristina Ariza-Carricondo, Michael Bahn, Bertrand Guenet, Per Gundersen, Gunnhildur E. Gunnarsdóttir, Thomas Kätterer, Zhanfeng Liu, Marja Maljanen, Sara Marañón-Jiménez, Kathiravan Meeran, Edda S. Oddsdóttir, Ivika Ostonen, Josep Peñuelas, Andreas Richter, Jordi Sardans, Páll Sigurðsson, Margaret S. Torn, Peter M. Van Bodegom, Erik Verbruggen, Tom W. N. Walker, Håkan Wallander, and Ivan A. Janssens
Biogeosciences, 19, 3381–3393, https://doi.org/10.5194/bg-19-3381-2022, https://doi.org/10.5194/bg-19-3381-2022, 2022
Short summary
Short summary
In subarctic grassland on a geothermal warming gradient, we found large reductions in topsoil carbon stocks, with carbon stocks linearly declining with warming intensity. Most importantly, however, we observed that soil carbon stocks stabilised within 5 years of warming and remained unaffected by warming thereafter, even after > 50 years of warming. Moreover, in contrast to the large topsoil carbon losses, subsoil carbon stocks remained unaffected after > 50 years of soil warming.
Roberto Pilli, Ramdane Alkama, Alessandro Cescatti, Werner A. Kurz, and Giacomo Grassi
Biogeosciences, 19, 3263–3284, https://doi.org/10.5194/bg-19-3263-2022, https://doi.org/10.5194/bg-19-3263-2022, 2022
Short summary
Short summary
To become carbon neutral by 2050, the European Union (EU27) forest C sink should increase to −450 Mt CO2 yr-1. Our study highlights that under current management practices (i.e. excluding any policy scenario) the forest C sink of the EU27 member states and the UK may decrease to about −250 Mt CO2eq yr-1 in 2050. The expected impacts of future climate change, however, add a considerable uncertainty, potentially nearly doubling or halving the sink associated with forest management.
Johnathan Daniel Maxey, Neil David Hartstein, Aazani Mujahid, and Moritz Müller
Biogeosciences, 19, 3131–3150, https://doi.org/10.5194/bg-19-3131-2022, https://doi.org/10.5194/bg-19-3131-2022, 2022
Short summary
Short summary
Deep coastal inlets are important sites for regulating land-based organic pollution before it enters coastal oceans. This study focused on how large climate forces, rainfall, and river flow impact organic loading and oxygen conditions in a coastal inlet in Tasmania. Increases in rainfall were linked to higher organic loading and lower oxygen in basin waters. Finally we observed a significant correlation between the Southern Annular Mode and oxygen concentrations in the system's basin waters.
Guang Gao, Tifeng Wang, Jiazhen Sun, Xin Zhao, Lifang Wang, Xianghui Guo, and Kunshan Gao
Biogeosciences, 19, 2795–2804, https://doi.org/10.5194/bg-19-2795-2022, https://doi.org/10.5194/bg-19-2795-2022, 2022
Short summary
Short summary
After conducting large-scale deck-incubation experiments, we found that seawater acidification (SA) increased primary production (PP) in coastal waters but reduced it in pelagic zones, which is mainly regulated by local pH, light intensity, salinity, and community structure. In future oceans, SA combined with decreased upward transports of nutrients may synergistically reduce PP in pelagic zones.
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Biogeosciences, 19, 2741–2757, https://doi.org/10.5194/bg-19-2741-2022, https://doi.org/10.5194/bg-19-2741-2022, 2022
Short summary
Short summary
This study is the first assessment to evaluate the interactions between river discharges, tides, and storm surges and how they can drive compound flooding in the Kapuas River delta. We successfully created a realistic hydrodynamic model whose domain covers the land–sea continuum using a wetting–drying algorithm in a data-scarce environment. We then proposed a new method to delineate compound flooding hazard zones along the river channels based on the maximum water level profiles.
Svenja Dobbert, Roland Pape, and Jörg Löffler
Biogeosciences, 19, 1933–1958, https://doi.org/10.5194/bg-19-1933-2022, https://doi.org/10.5194/bg-19-1933-2022, 2022
Short summary
Short summary
Understanding how vegetation might respond to climate change is especially important in arctic–alpine ecosystems, where major shifts in shrub growth have been observed. We studied how such changes come to pass and how future changes might look by measuring hourly variations in the stem diameter of dwarf shrubs from one common species. From these data, we are able to discern information about growth mechanisms and can thus show the complexity of shrub growth and micro-environment relations.
Jody Daniel, Rebecca C. Rooney, and Derek T. Robinson
Biogeosciences, 19, 1547–1570, https://doi.org/10.5194/bg-19-1547-2022, https://doi.org/10.5194/bg-19-1547-2022, 2022
Short summary
Short summary
The threat posed by climate change to prairie pothole wetlands is well documented, but gaps remain in our ability to make meaningful predictions about how prairie pothole wetlands will respond. We integrate aspects of topography, land cover/land use and climate to model the permanence class of tens of thousands of wetlands at the western edge of the Prairie Pothole Region.
Ádám T. Kocsis, Qianshuo Zhao, Mark J. Costello, and Wolfgang Kiessling
Biogeosciences, 18, 6567–6578, https://doi.org/10.5194/bg-18-6567-2021, https://doi.org/10.5194/bg-18-6567-2021, 2021
Short summary
Short summary
Biodiversity is under threat from the effects of global warming, and assessing the effects of climate change on areas of high species richness is of prime importance to conservation. Terrestrial and freshwater rich spots have been and will be less affected by climate change than other areas. However, marine rich spots of biodiversity are expected to experience more pronounced warming.
Rob Wilson, Kathy Allen, Patrick Baker, Gretel Boswijk, Brendan Buckley, Edward Cook, Rosanne D'Arrigo, Dan Druckenbrod, Anthony Fowler, Margaux Grandjean, Paul Krusic, and Jonathan Palmer
Biogeosciences, 18, 6393–6421, https://doi.org/10.5194/bg-18-6393-2021, https://doi.org/10.5194/bg-18-6393-2021, 2021
Short summary
Short summary
We explore blue intensity (BI) – a low-cost method for measuring ring density – to enhance palaeoclimatology in Australasia. Calibration experiments, using several conifer species from Tasmania and New Zealand, model 50–80 % of the summer temperature variance. The implications of these results have profound consequences for high-resolution paleoclimatology in Australasia, as the speed and cheapness of BI generation could lead to a step change in our understanding of past climate in the region.
Alex R. Quijada-Rodriguez, Pou-Long Kuan, Po-Hsuan Sung, Mao-Ting Hsu, Garett J. P. Allen, Pung Pung Hwang, Yung-Che Tseng, and Dirk Weihrauch
Biogeosciences, 18, 6287–6300, https://doi.org/10.5194/bg-18-6287-2021, https://doi.org/10.5194/bg-18-6287-2021, 2021
Short summary
Short summary
Anthropogenic CO2 is chronically acidifying aquatic ecosystems. We aimed to determine the impact of future freshwater acidification on the physiology and behaviour of an important aquaculture crustacean, Chinese mitten crabs. We report that elevated freshwater CO2 levels lead to impairment of calcification, locomotor behaviour, and survival and reduced metabolism in this species. Results suggest that present-day calcifying invertebrates could be heavily affected by freshwater acidification.
Junrong Zha and Qianlai Zhuang
Biogeosciences, 18, 6245–6269, https://doi.org/10.5194/bg-18-6245-2021, https://doi.org/10.5194/bg-18-6245-2021, 2021
Short summary
Short summary
This study incorporated moss into an extant biogeochemistry model to simulate the role of moss in carbon dynamics in the Arctic. The interactions between higher plants and mosses and their competition for energy, water, and nutrients are considered in our study. We found that, compared with the previous model without moss, the new model estimated a much higher carbon accumulation in the region during the last century and this century.
Maria Belke-Brea, Florent Domine, Ghislain Picard, Mathieu Barrere, and Laurent Arnaud
Biogeosciences, 18, 5851–5869, https://doi.org/10.5194/bg-18-5851-2021, https://doi.org/10.5194/bg-18-5851-2021, 2021
Short summary
Short summary
Expanding shrubs in the Arctic change snowpacks into a mix of snow, impurities and buried branches. Snow is a translucent medium into which light penetrates and gets partly absorbed by branches or impurities. Measurements of light attenuation in snow in Northern Quebec, Canada, showed (1) black-carbon-dominated light attenuation in snowpacks without shrubs and (2) buried branches influence radiation attenuation in snow locally, leading to melting and pockets of large crystals close to branches.
Sazlina Salleh and Andrew McMinn
Biogeosciences, 18, 5313–5326, https://doi.org/10.5194/bg-18-5313-2021, https://doi.org/10.5194/bg-18-5313-2021, 2021
Short summary
Short summary
The benthic diatom communities in Tanjung Rhu, Malaysia, were regularly exposed to high light and temperature variability during the tidal cycle, resulting in low photosynthetic efficiency. We examined the impact of high temperatures on diatoms' photosynthetic capacities, and temperatures beyond 50 °C caused severe photoinhibition. At the same time, those diatoms exposed to temperatures of 40 °C did not show any sign of photoinhibition.
Cornelius Senf and Rupert Seidl
Biogeosciences, 18, 5223–5230, https://doi.org/10.5194/bg-18-5223-2021, https://doi.org/10.5194/bg-18-5223-2021, 2021
Short summary
Short summary
Europe was affected by an extreme drought in 2018. We show that this drought has increased forest disturbances across Europe, especially central and eastern Europe. Disturbance levels observed 2018–2020 were the highest on record for 30 years. Increased forest disturbances were correlated with low moisture and high atmospheric water demand. The unprecedented impacts of the 2018 drought on forest disturbances demonstrate an urgent need to adapt Europe’s forests to a hotter and drier future.
Jessica L. McCarty, Juha Aalto, Ville-Veikko Paunu, Steve R. Arnold, Sabine Eckhardt, Zbigniew Klimont, Justin J. Fain, Nikolaos Evangeliou, Ari Venäläinen, Nadezhda M. Tchebakova, Elena I. Parfenova, Kaarle Kupiainen, Amber J. Soja, Lin Huang, and Simon Wilson
Biogeosciences, 18, 5053–5083, https://doi.org/10.5194/bg-18-5053-2021, https://doi.org/10.5194/bg-18-5053-2021, 2021
Short summary
Short summary
Fires, including extreme fire seasons, and fire emissions are more common in the Arctic. A review and synthesis of current scientific literature find climate change and human activity in the north are fuelling an emerging Arctic fire regime, causing more black carbon and methane emissions within the Arctic. Uncertainties persist in characterizing future fire landscapes, and thus emissions, as well as policy-relevant challenges in understanding, monitoring, and managing Arctic fire regimes.
Alexander J. Winkler, Ranga B. Myneni, Alexis Hannart, Stephen Sitch, Vanessa Haverd, Danica Lombardozzi, Vivek K. Arora, Julia Pongratz, Julia E. M. S. Nabel, Daniel S. Goll, Etsushi Kato, Hanqin Tian, Almut Arneth, Pierre Friedlingstein, Atul K. Jain, Sönke Zaehle, and Victor Brovkin
Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, https://doi.org/10.5194/bg-18-4985-2021, 2021
Short summary
Short summary
Satellite observations since the early 1980s show that Earth's greening trend is slowing down and that browning clusters have been emerging, especially in the last 2 decades. A collection of model simulations in conjunction with causal theory points at climatic changes as a key driver of vegetation changes in natural ecosystems. Most models underestimate the observed vegetation browning, especially in tropical rainforests, which could be due to an excessive CO2 fertilization effect in models.
Vincent Niderkorn, Annette Morvan-Bertrand, Aline Le Morvan, Angela Augusti, Marie-Laure Decau, and Catherine Picon-Cochard
Biogeosciences, 18, 4841–4853, https://doi.org/10.5194/bg-18-4841-2021, https://doi.org/10.5194/bg-18-4841-2021, 2021
Short summary
Short summary
Climate change can change vegetation characteristics in grasslands with a potential impact on forage chemical composition and quality, as well as its use by ruminants. Using controlled conditions mimicking a future climatic scenario, we show that forage quality and ruminant digestion are affected in opposite ways by elevated atmospheric CO2 and an extreme event (heat wave, severe drought), indicating that different factors of climate change have to be considered together.
Verónica Pancotto, David Holl, Julio Escobar, María Florencia Castagnani, and Lars Kutzbach
Biogeosciences, 18, 4817–4839, https://doi.org/10.5194/bg-18-4817-2021, https://doi.org/10.5194/bg-18-4817-2021, 2021
Short summary
Short summary
We investigated the response of a wetland plant community to elevated temperature conditions in a cushion bog on Tierra del Fuego, Argentina. We measured carbon dioxide fluxes at experimentally warmed plots and at control plots. Warmed plant communities sequestered between 55 % and 85 % less carbon dioxide than untreated control cushions over the main growing season. Our results suggest that even moderate future warming could decrease the carbon sink function of austral cushion bogs.
Cited articles
Abiodun, B. J., Salami, A. T., Matthew, O. J., and Odedokun, S.: Potential impacts of afforestation on climate change and extreme events in Nigeria, Clim. Dynam., 41, 277–293, https://doi.org/10.1007/s00382-012-1523-9, 2013.
Anwar, S. A. and Diallo, I.: Modelling the Tropical African Climate using a state-of-the-art coupled regional climate-vegetation model, Clim. Dynam., 58, 97–113, https://doi.org/10.1007/s00382-021-05892-9, 2022.
Arora, V. K. and Montenegro, A.: Small temperature benefits provided by realistic afforestation efforts, Nat. Geosci., 4, 514–518, https://doi.org/10.1038/ngeo1182, 2011.
Bastin, J.-F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D., Zohner, C. M., and Crowther, T. W.: The global tree restoration potential, Science, 365, 76–79, https://doi.org/10.1126/science.aax0848, 2019.
Bhattacharya, B., Mohanty, S., and Singh, C.: Assessment of the potential of CMIP6 models in simulating the sea surface temperature variability over the tropical Indian Ocean, Theor. Appl. Climatol., 148, 585–602, https://doi.org/10.1007/s00704-022-03952-6, 2022.
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.
Bowden, J. H., Terando, A. J., Misra, V., Wootten, A., Bhardwaj, A., Boyles, R., Gould, W., Collazo, J. A., and Spero, T. L.: High-resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the US Virgin Islands, Int. J. Climatol., 41, 1305–1327, https://doi.org/10.1002/joc.6810, 2021.
Box, E. O.: Predicting physiognomic vegetation types with climate variables, Vegetatio, 45, 127–139, https://doi.org/10.1007/BF00119222, 1981.
Breil, M., Davin, E. L., and Rechid, D.: What determines the sign of the evapotranspiration response to afforestation in European summer?, Biogeosciences, 18, 1499–1510, https://doi.org/10.5194/bg-18-1499-2021, 2021.
Breil, M., Schneider, V. K. M., and Pinto, J. G.: The effect of forest cover changes on the regional climate conditions in Europe during the period 1986–2015, Biogeosciences, 21, 811–824, https://doi.org/10.5194/bg-21-811-2024, 2024.
Brockerhoff, E. G., Barbaro, L., Castagneyrol, B., Forrester, D. I., Gardiner, B., González-Olabarria, J. R., Lyver, P. O., Meurisse, N., Oxbrough, A., Taki, H., Thompson, I. D., van der Plas, F., and Jactel, H.: Forest biodiversity, ecosystem functioning and the provision of ecosystem services, Biodivers. Conserv., 26, 3005–3035, https://doi.org/10.1007/s10531-017-1453-2, 2017.
Bukovsky, M. S. and Mearns, L. O.: Regional climate change projections from NA-CORDEX and their relation to climate sensitivity, Climatic Change, 162, 645–665, https://doi.org/10.1007/s10584-020-02835-x, 2020.
Cardoso, R. M., Soares, P. M., Lima, D. C., and Miranda, P.: Mean and extreme temperatures in a warming climate: EURO CORDEX and WRF regional climate high-resolution projections for Portugal, Clim. Dynam., 52, 129–157, https://doi.org/10.1007/s00382-018-4124-4, 2019.
Case, J. L., Crosson, W. L., Kumar, S. V., Lapenta, W. M., and Peters-Lidard, C. D.: Impacts of high-resolution land surface initialization on regional sensible weather forecasts from the WRF model, J. Hydrometeorol., 9, 1249–1266, https://doi.org/10.1175/2008JHM990.1, 2008.
Chen, C., Park, T., Wang, X., Piao, S., Xu, B., Chaturvedi, R. K., Fuchs, R., Brovkin, V., Ciais, P., Fensholt, R., Tømmervik, H., Bala, G., Zhu, Z., Nemani, R. R., and Myneni, R. B.: China and India lead in greening of the world through land-use management, Nat. Sustain., 2, 122–129, https://doi.org/10.1038/s41893-019-0220-7, 2019.
Cohen, J.: A coefficient of agreement for nominal scales, Educ. Psychol. Meas., 20, 37–46, https://doi.org/10.1177/001316446002000104, 1960.
Collins, W. D., Rasch, P., Boville, B., Hack, J., McCaa, J., Williamson, D., Kiehl, J., Briegleb, B., Bitz, C., Lin, S.-J., Zhang, M., and Dai, Y.: Description of the NCAR community atmosphere model (CAM 3.0), Natl. Cent. for Atmos. Res., Boulder, Colorado, 226, 1326–1334, https://doi.org/10.5065/D63N21CH, 2004.
Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A., Brovkin, V., Cox, P. M., Fisher, V., Foley, J. A., Friend, A. D., Kucharik, C., Lomas, M. R., Ramankutty, N., Sitch, S., Smith, B., White, A., and Young-Molling, C.: Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models, Glob. Change Biol., 7, 357–373, https://doi.org/10.1046/j.1365-2486.2001.00383.x, 2001.
Crespo, N. M., da Silva, N. P., Palmeira, R. M. J., Cardoso, A. A., Kaufmann, C. L. G., Lima, J. A. M., Andrioni, M., de Camargo, R. and da Rocha, R. P.: Western South Atlantic Climate Experiment (WeSACEx): Extreme winds and waves over the Southeastern Brazilian sedimentary basins, Clim. Dynam., 60, 571–588, https://doi.org/10.1007/s00382-022-06340-y, 2023.
Dan, L., Ji, J., and Zhang, P.: The soil moisture of China in a high resolution climate-vegetation model, Adv. Atmos. Sci., 22, 720–729, https://doi.org/10.1007/BF02918715, 2005.
de Lima, R. F., de Oliveira Aparecido, L. E., Lorençone, J. A., Lorençone, P. A., Torsoni, G. B., da Silva Cabral Moraes, J. R., and de Meneses, K. C.: Assessing life zone changes under climate change scenarios in Brazil, Theor. Appl. Climatol., 149, 1687–1703, https://doi.org/10.1007/s00704-022-04133-1, 2022.
Diasso, U. and Abiodun, B. J.: Future impacts of global warming and reforestation on drought patterns over West Africa, Theor. Appl. Climatol., 133, 647–662, https://doi.org/10.1007/s00704-017-2209-3, 2018.
Elsen, P. R., Saxon, E. C., Simmons, B. A., Ward, M., Williams, B. A., Grantham, H. S., Kark, S., Levin, N., Perez-Hammerle, K. V., Reside, A. E., and Watson, J. E. M.: Accelerated shifts in terrestrial life zones under rapid climate change, Glob. Change Biol., 28, 918–935, https://doi.org/10.1111/gcb.15962, 2022.
Fan, Z. and Bai, X.: Scenarios of potential vegetation distribution in the different gradient zones of Qinghai-Tibet Plateau under future climate change, Sci. Total Environ., 796, 148918, https://doi.org/10.1016/j.scitotenv.2021.148918, 2021.
Fan, Z., Fan, B., and Yue, T.: Terrestrial ecosystem scenarios and their response to climate change in Eurasia, Sci. China Earth Sci., 62, 1607–1618, https://doi.org/10.1007/s11430-018-9374-3, 2019.
Fang, J., Piao, S., Zhou, L., He, J., Wei, F., Myneni, R. B., Tucker, C. J., and Tan, K.: Precipitation patterns alter growth of temperate vegetation, Geophys. Res. Lett., 32, L21411, https://doi.org/10.1029/2005GL024231, 2005.
Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch, S., and Haxeltine, A.: An integrated biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics, Global Biogeochem. Cy., 10, 603–628, https://doi.org/10.1029/96GB02692, 1996.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010 (data available at: https://e4ftl01.cr.usgs.gov/MOTA/MCD12Q1.061/, last access: 30 March 2022).
Fu, B., Liu, Y., and Meadows, M. E.: Ecological restoration for sustainable development in China, Natl. Sci. Rev., 10, nwad033, https://doi.org/10.1093/nsr/nwad033, 2023.
Fu, Y., Ma, Y., Zhong, L., Yang, Y., Guo, X., Wang, C., Xu, X., Yang, K., Xu, X., Liu, J., Fan G., Li, Y., and Wang, D.: Land-surface processes and summer-cloud-precipitation characteristics in the Tibetan Plateau and their effects on downstream weather: a review and perspective, Natl. Sci. Rev., 7, 500–515, https://doi.org/10.1093/nsr/nwz226, 2020.
Gao, S.: Dynamical downscaling of surface air temperature and precipitation using RegCM4 and WRF over China, Clim. Dynam., 55, 1283–1302, https://doi.org/10.1007/s00382-020-05326-y, 2020.
Gao, S., Zhu, S., and Yu, H.: Dynamical downscaling of temperature extremes over China using the WRF model driven by different lateral boundary conditions, Atmos. Res., 278, 106348, https://doi.org/10.1016/j.atmosres.2022.106348, 2022.
Gao, Z., Yan, X., Dong, S., Luo, N., and Song, S.: Object-based evaluation of rainfall forecasts over eastern China by eight cumulus parameterization schemes in the WRF model, Atmos. Res., 284, 106618, https://doi.org/10.1016/j.atmosres.2023.106618, 2023.
Gbode, I. E., Dudhia, J., Ogunjobi, K. O., and Ajayi, V. O.: Sensitivity of different physics schemes in the WRF model during a West African monsoon regime, Theor. Appl. Climatol., 136, 733–751, https://doi.org/10.1007/s00704-018-2538-x, 2019.
Ge, J., Pitman, A. J., Guo, W., Zan, B., and Fu, C.: Impact of revegetation of the Loess Plateau of China on the regional growing season water balance, Hydrol. Earth Syst. Sci., 24, 515–533, https://doi.org/10.5194/hess-24-515-2020, 2020.
Gebrechorkos, S., Hülsmann, S., and Bernhofer, C.: Regional climate projections for impact assessment studies in East Africa, Environ. Res. Lett., 14, 044031, https://doi.org/10.1088/1748-9326/ab055a, 2019.
Gebrechorkos, S., Leyland, J., Slater, L., Wortmann, M., Ashworth, P. J., Bennett, G. L., Boothroyd, R., Cloke, H., Delorme, P., Griffith, H., Hardy, R., Hawker, L., McLelland, S., Neal, J., Nicholas, A., Tatem, A. J., Vahidi, E., Parsons, D. R., and Darby, S. E.: A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses, Sci. Data, 10, 611, https://doi.org/10.1038/s41597-023-02528-x, 2023.
Giorgi, F. and Mearns, L. O.: Introduction to special section: Regional climate modeling revisited, J. Geophys. Res.-Atmos., 104, 6335–6352, https://doi.org/10.1029/98JD02072, 1999.
Grell, G. A. and Dévényi, D.: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques, Geophys. Res. Lett., 29, 1693, https://doi.org/10.1029/2002GL015311, 2002.
Gundersen, P., Thybring, E. E., Nord-Larsen, T., Vesterdal, L., Nadelhoffer, K. J., and Johannsen, V. K.: Old-growth forest carbon sinks overestimated, Nature, 591, E21–E23, https://doi.org/10.1038/s41586-021-03266-z, 2021.
Han, Y., Zhang, M. Z., Xu, Z., and Guo, W.: Assessing the performance of 33 CMIP6 models in simulating the large-scale environmental fields of tropical cyclones, Clim. Dynam., 58, 1683–1698, https://doi.org/10.1007/s00382-021-05986-4, 2022.
Hansen, M. C., Townshend, J. R., DeFries, R. S., and Carroll, M.: Estimation of tree cover using MODIS data at global, continental and regional/local scales, Int. J. Remote Sens., 26, 4359–4380, https://doi.org/10.1080/01431160500113435, 2005.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020 (data available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last access: 20 March 2022).
Hickler, T., Vohland, K., Feehan, J., Miller, P. A., Smith, B., Costa, L., Giesecke, T., Fronzek, S., Carter, T. R., Cramer, W., Kühn, I., and Sykes, M. T.: Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model, Global Ecol. Biogeogr., 21, 50–63, https://doi.org/10.1111/j.1466-8238.2010.00613.x, 2012.
Hinze, J., Albrecht, A., and Michiels, H. G.: Climate-Adapted Potential Vegetation – A European Multiclass Model Estimating the Future Potential of Natural Vegetation, Forests, 14, 239, https://doi.org/10.3390/f14020239, 2023.
Holdridge, L. R.: Determination of world plant formations from simple climatic data, Science, 105, 367–368, https://doi.org/10.1126/science.105.2727.367, 1947.
Hong, S. Y., Dudhia, J., and Chen, S. H.: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation, Mon. Weather Rev., 132, 103–120, https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2, 2004.
Horvath, P., Tang, H., Halvorsen, R., Stordal, F., Tallaksen, L. M., Berntsen, T. K., and Bryn, A.: Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models, Biogeosciences, 18, 95–112, https://doi.org/10.5194/bg-18-95-2021, 2021.
Hou, H., Zhou, B. B., Pei, F., Hu, G., Su, Z., Zeng, Y., Zhang, H., Gao, Y., Luo, M., and Li, X.: Future land use/land cover change has nontrivial and potentially dominant impact on global gross primary productivity, Earths Future, 10, e2021EF002628, https://doi.org/10.1029/2021EF002628, 2022.
Hu, Y., Zhang, X. Z., Mao, R., Gong, D. Y., Liu, H. B., and Yang, J.: Modeled responses of summer climate to realistic land use/cover changes from the 1980s to the 2000s over eastern China, J. Geophys. Res.-Atmos., 120, 167–179, https://doi.org/10.1002/2014JD022288, 2015.
Hu, Y., Li, H., Wu, D., Chen, W., Zhao, X., Hou, M., Li, A., and Zhu, Y.: LAI-indicated vegetation dynamic in ecologically fragile region: A case study in the Three-North Shelter Forest program region of China, Ecol. Indic., 120, 106932, https://doi.org/10.1016/j.ecolind.2020.106932, 2021.
Huang, D. and Gao, S.: Impact of different reanalysis data on WRF dynamical downscaling over China, Atmos. Res., 200, 25–35, https://doi.org/10.1016/j.atmosres.2017.09.017, 2018.
Huang, L., Wang, B., Niu, X., Gao, P., and Song, Q.: Changes in ecosystem services and an analysis of driving factors for China's Natural Forest Conservation Program, Ecol. Evol., 9, 3700–3716, https://doi.org/10.1002/ece3.4925, 2019.
Hui, P., Tang, J., Wang, S., Niu, X., Zong, P., and Dong, X.: Climate change projections over China using regional climate models forced by two CMIP5 global models. Part II: projections of future climate, Int. J. Climatol., 38, e78–e94, https://doi.org/10.1002/joc.5409, 2018.
Jayakrishnan, K. U. and Bala, G.: A comparison of the climate and carbon cycle effects of carbon removal by afforestation and an equivalent reduction in fossil fuel emissions, Biogeosciences, 20, 1863–1877, https://doi.org/10.5194/bg-20-1863-2023, 2023.
Ji, Z. and Kang, S.: Projection of snow cover changes over China under RCP scenarios, Clim. Dynam., 41, 589–600, https://doi.org/10.1007/s00382-012-1473-2, 2013.
Jiang, Y., Zhuang, Q., Schaphoff, S., Sitch, S., Sokolov, A., Kicklighter, D., and Melillo, J.: Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model, Ecol. Evol., 2, 593–614, https://doi.org/10.1002/ece3.85, 2012.
Kamruzzaman, M., Shahid, S., Roy, D. K., Islam, A. R. M. T., Hwang, S., Cho, J., Zaman, M. A. U., Sultana, T., Rashid, T., and Akter, F.: Assessment of CMIP6 global climate models in reconstructing rainfall climatology of Bangladesh, Int. J. Climatol., 42, 3928–3953, https://doi.org/10.1002/joc.7452, 2022.
Kaplan, J. O.: Geophysical applications of vegetation modelling, Lund University, ISBN 91-7874-089-4, 2001.
Karim, R., Tan, G., Ayugi, B., Babaousmail, H., and Liu, F.: Evaluation of historical CMIP6 model simulations of seasonal mean temperature over Pakistan during 1970–2014, Atmosphere, 11, 1005, https://doi.org/10.3390/atmos11091005, 2020.
Karypidou, M. C., Sobolowski, S. P., Sangelantoni, L., Nikulin, G., and Katragkou, E.: The impact of lateral boundary forcing in the CORDEX-Africa ensemble over southern Africa, Geosci. Model Dev., 16, 1887–1908, https://doi.org/10.5194/gmd-16-1887-2023, 2023.
Kebe, I., Sylla, M. B., Omotosho, J. A., Nikiema, P. M., Gibba, P., and Giorgi, F.: Impact of GCM boundary forcing on regional climate modeling of West African summer monsoon precipitation and circulation features, Clim. Dynam., 48, 1503–1516, https://doi.org/10.1007/s00382-016-3156-x, 2017.
Knist, S., Goergen, K., and Simmer, C.: Evaluation and projected changes of precipitation statistics in convection-permitting WRF climate simulations over Central Europe, Clim. Dynam., 55, 325–341, https://doi.org/10.1007/s00382-018-4147-x, 2020.
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Kummu, M., Heino, M., Taka, M., Varis, O., and Viviroli, D.: Climate change risks pushing one-third of global food production outside the safe climatic space, One Earth, 4, 720–729, https://doi.org/10.1016/j.oneear.2021.04.017, 2021.
Landis, J. R. and Koch, G. G.: The measurement of observer agreement for categorical data, Biometrics, 33, 159–174, https://doi.org/10.2307/2529310, 1977.
Lewis, S. L., Mitchard, E. T. A., Prentice, C., Maslin, M., and Poulter, B.: Comment on “The global tree restoration potential”, Science, 366, eaaz0388, https://doi.org/10.1126/science.aaz0388, 2019.
Li, S., An, P., Pan, Z., Wang, F., Li, X., and Liu, Y.: Farmers' initiative on adaptation to climate change in the Northern Agro-pastoral Ecotone, Int. J. Disast. Risk Re., 12, 278–284, https://doi.org/10.1016/j.ijdrr.2015.02.002, 2015.
Li, S., Zhang, J., Henchiri, M., Cao, D., Zhang, S., Bai, Y., and Yang, S.: Spatiotemporal Variations of Chinese Terrestrial Ecosystems in Response to Land Use and Future Climate Change, Atmosphere, 13, 1024, https://doi.org/10.3390/atmos13071024, 2022.
Lin, C., Chen, D., Yang, K., and Ou, T.: Impact of model resolution on simulating the water vapor transport through the central Himalayas: implication for models' wet bias over the Tibetan Plateau, Clim. Dynam., 51, 3195–3207, https://doi.org/10.1007/s00382-018-4074-x, 2018.
Liu, H.: It is difficult for China's greening through large-scale afforestation to cross the Hu Line, Sci. China Earth. Sci., 62, 1662–1664, https://doi.org/10.1007/s11430-019-9381-3, 2019.
Lin, H., Tang, J., Wang, S., Wang, S., and Dong, G.: Deep learning downscaled high-resolution daily near surface meteorological datasets over East Asia, Sci. Data, 10, 890, https://doi.org/10.1038/s41597-023-02805-9, 2023.
Liu, W., Wang, G., Yu, M., Chen, H., and Jiang, Y.: Multimodel future projections of the regional vegetation-climate system over East Asia: Comparison between two ensemble approaches, J. Geophys. Res.-Atmos., 125, e2019JD031967, https://doi.org/10.1029/2019JD031967, 2020a.
Liu, W., Wang, G., Yu, M., Chen, H., Jiang, Y., Yang, M., and Shi, Y.: Projecting the future vegetation–climate system over East Asia and its RCP-dependence, Clim. Dynam., 55, 2725–2742, https://doi.org/10.1007/s00382-020-05411-2, 2020b.
Liu, Y., Ge, J., Guo, W., Cao, Y., Chen, C., Luo, X., Yang, L., and Wang, S.: Revisiting biophysical impacts of greening on precipitation over the Loess Plateau of China using WRF with water vapor tracers, Geophys. Res. Lett., 50, e2023GL102809, https://doi.org/10.1029/2023GL102809, 2023.
Liu, Z., Deng, Z., He, G., Wang, H., Zhang, X., Lin, J., Qi, Y., and Liang, X.: Challenges and opportunities for carbon neutrality in China, Nat. Rev. Earth Env., 3, 141–155, https://doi.org/10.1038/s43017-021-00244-x, 2022.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L. W. M. J., and Merchant, J. W.: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, https://doi.org/10.1080/014311600210191, 2000.
Lu, Z., Han, Y., and Liu, Y.: Improving the Ramer scheme for diagnosis of freezing rain in China, Atmos. Res., 254, 105520, https://doi.org/10.1016/j.atmosres.2021.105520, 2021.
Lucas-Picher, P., Argüeso, D., Brisson, E., Tramblay, Y., Berg, P., Lemonsu, A., Kotlarski, S., and Caillaud, C.: Convection-permitting modeling with regional climate models: Latest developments and next steps, WIRes Clim. Change, 12, e731, https://doi.org/10.1002/wcc.731, 2021.
Ma, M., Tang, J., Ou, T., and Zhou, P.: High-resolution climate projection over the Tibetan Plateau using WRF forced by bias-corrected CESM, Atmos. Res., 286, 106670, https://doi.org/10.1016/j.atmosres.2023.106670, 2023.
Ma, W., Jia, G., and Zhang, A.: Multiple satellite-based analysis reveals complex climate effects of temperate forests and related energy budget, J. Geophys. Res.-Atmos., 122, 3806–3820, https://doi.org/10.1002/2016JD026278, 2017.
Mallard, M. S. and Spero, T. L.: Effects of mosaic land use on dynamically downscaled WRF simulations of the contiguous United States, J. Geophys. Res.-Atmos., 124, 9117–9140, https://doi.org/10.1029/2018JD029755, 2019.
Martens, C., Hickler, T., Davis-Reddy, C., Engelbrecht, F., Higgins, S. I., Von Maltitz, G. P., Midgley, G. F., Pfeiffer, M., and Scheiter, S.: Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies, Glob. Change Biol., 27, 340–358, https://doi.org/10.1111/gcb.15390, 2021.
MathWorks Inc.: MATLAB version 2020a, login required, Natick, Massachusetts, https://www.mathworks.com/login?uri=%2Fdownloads%2Fweb_downloads (last access: 20 July 2021), 2020.
Meng, X., Lyu, S., Zhang, T., Zhao, L., Li, Z., Han, B., Li, S., Ma, D., Chen, H., Ao, Y., Luo, S., Shen, Y., Guo, J., and Wen, L.: Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model, Environ. Res. Lett., 13, 044028, https://doi.org/10.1088/1748-9326/aab44a, 2018.
Mishra, V., Kumar, D., Ganguly, A. R., Sanjay, J., Mujumdar, M., Krishnan, R., and Shah, R. D.: Reliability of regional and global climate models to simulate precipitation extremes over India, J. Geophys. Res.-Atmos., 119, 9301–9323, https://doi.org/10.1002/2014JD021636, 2014.
Moalafhi, D. B., Evans, J. P., and Sharma, A.: Influence of reanalysis datasets on dynamically downscaling the recent past, Clim. Dynam., 49, 1239–1255, https://doi.org/10.1007/s00382-016-3378-y, 2017.
Moore, J. C., Chen, Y., Cui, X., Yuan, W., Dong, W., Gao, Y., and Shi, P.: Will China be the first to initiate climate engineering?, Earths Future, 4, 588–595, https://doi.org/10.1002/2016EF000402, 2016.
Moustakis, Y., Papalexiou, S. M., Onof, C. J., and Paschalis, A.: Seasonality, intensity, and duration of rainfall extremes change in a warmer climate, Earths Future, 9, e2020EF001824, https://doi.org/10.1029/2020EF001824, 2021.
Moustakis, Y., Fatichi, S., Onof, C., and Paschalis, A.: Insensitivity of ecosystem productivity to predicted changes in fine-scale rainfall variability, J. Geophys. Res.-Biogeo., 127, e2021JG006735, https://doi.org/10.1029/2021JG006735, 2022.
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M., Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine, T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E., Stemmler, I., Tian, F., and Marotzke, J.: A higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1. 2-HR), J. Adv. Model. Earth Sy., 10, 1383–1413, https://doi.org/10.1029/2017MS001217, 2018 (data available at: https://esgf.nci.org.au/search/cmip6-nci/, last access: 11 May 2022).
Naik, M. and Abiodun, B. J.: Potential impacts of forestation on future climate change in Southern Africa, Int. J. Climatol., 36, 4560–4576, https://doi.org/10.1002/joc.4652, 2016.
Navarro, A., Merino, A., Sánchez, J. L., García-Ortega, E., Martín, R., and Tapiador, F. J.: Towards better characterization of global warming impacts in the environment through climate classifications with improved global models, Int. J. Climatol., 42, 5197–5217, https://doi.org/10.1002/joc.7527, 2022.
Neilson, R. P., King, G. A., and Koerper, G.: Toward a rule-based biome model, Landscape Ecol., 7, 27–43, https://doi.org/10.1007/BF02573955, 1992.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Niu, X., Tang, J., Wang, S., and Fu, C.: Impact of future land use and land cover change on temperature projections over East Asia, Clim. Dynam., 52, 6475–6490, https://doi.org/10.1007/s00382-018-4525-4, 2019.
Noh, Y., Cheon, W. G., Hong, S. Y., and Raasch, S.: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data, Bound.-Lay. Meteorol., 107, 401–427, https://doi.org/10.1023/A:1022146015946, 2003.
Odoulami, R. C., Abiodun, B. J., and Ajayi, A. E.: Modelling the potential impacts of afforestation on extreme precipitation over West Africa, Clim. Dynam., 52, 2185–2198, https://doi.org/10.1007/s00382-018-4248-6, 2019.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Ou, T., Chen, D., Chen, X., Lin, C., Yang, K., Lai, H. W., and Zhang, F.: Simulation of summer precipitation diurnal cycles over the Tibetan Plateau at the gray-zone grid spacing for cumulus parameterization, Clim. Dynam., 54, 3525–3539, https://doi.org/10.1007/s00382-020-05181-x, 2020.
Ozturk, T., Turp, M. T., Türkeş, M., and Kurnaz, M. L.: Future projections of temperature and precipitation climatology for CORDEX-MENA domain using RegCM4.4, Atmos. Res., 206, 87–107, https://doi.org/10.1016/j.atmosres.2018.02.009, 2018.
Parsons, L. A.: Implications of CMIP6 projected drying trends for 21st century Amazonian drought risk, Earths Future, 8, e2020EF001608, https://doi.org/10.1029/2020EF001608, 2020.
Peng, S., Piao, S., Zeng, Z., Ciais, P., Zhou, L., Li, L. Z. X., Myneni, R. B., Yin, Y., and Zeng, H.: Afforestation in China cools local land surface temperature, P. Natl. Acad. Sci. USA, 111, 2915–2919, https://doi.org/10.1073/pnas.1315126111, 2014.
Piao, S., Wang, X., Ciais, P., Zhu, B., Wang, T. A. O., and Liu, J. I. E.: Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006, Glob. Change Biol., 17, 3228–3239, https://doi.org/10.1111/j.1365-2486.2011.02419.x, 2011.
Politi, N., Vlachogiannis, D., Sfetsos, A., and Nastos, P. T.: High-resolution dynamical downscaling of ERA-Interim temperature and precipitation using WRF model for Greece, Clim. Dynam., 57, 799–825, https://doi.org/10.1007/s00382-021-05741-9, 2021.
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015.
Qiu, Y., Feng, J., Yan, Z., Wang, J., and Li, Z.: High-resolution dynamical downscaling for regional climate projection in Central Asia based on bias-corrected multiple GCMs, Clim. Dynam., 58, 777–791, https://doi.org/10.1007/s00382-021-05934-2, 2022.
Rahimi, S. R., Wu, C., Liu, X., and Brown, H.: Exploring a variable-resolution approach for simulating regional climate over the Tibetan Plateau using VR-CESM, J. Geophys. Res.-Atmos., 124, 4490–4513, https://doi.org/10.1029/2018JD028925, 2019.
Rohatyn, S., Yakir, D., Rotenberg, E., and Carmel, Y.: Limited climate change mitigation potential through forestation of the vast dryland regions, Science, 377, 1436–1439, https://doi.org/10.1126/science.abm9684, 2022.
Sato, T., Kimura, F., and Kitoh, A.: Projection of global warming onto regional precipitation over Mongolia using a regional climate model, J. Hydrol., 333, 144–154, https://doi.org/10.1016/j.jhydrol.2006.07.023, 2007.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003.
Shi, S. and Han, P.: Estimating the soil carbon sequestration potential of China's Grain for Green Project, Global Biogeochem. Cy., 28, 1279–1294, https://doi.org/10.1002/2014GB004924, 2014.
Shuaifeng, S.: Mapping the Future Afforestation Distribution of China Constrained by National Afforestation Plan and Climate Change (Version 1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.10900150, 2024.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X. Y.: A description of the advanced research WRF model version 4, National Center for Atmospheric Research, Boulder, CO, USA, 145 pp., https://doi.org/10.5065/1dfh-6p97, 2019 (data available at: https://www2.mmm.ucar.edu/wrf/users/, last access: 18 March 2022).
Song, S. and Yan, X.: Projected changes and uncertainty in cold surges over northern China using the CMIP6 weighted multi-model ensemble, Atmos. Res., 278, 106334, https://doi.org/10.1016/j.atmosres.2022.106334, 2022.
Song, S., Zhang, X., Gao, Z., and Yan, X.: Evaluation of atmospheric circulations for dynamic downscaling in CMIP6 models over East Asia, Clim. Dynam., 60, 2437–2458, https://doi.org/10.1007/s00382-022-06465-0, 2023.
State Council of China: Action Plan for Carbon Dioxide Peaking Before 2030, 24 October 2021, https://www.gov.cn/gongbao/content/2021/content_5649731.htm?eqid=e82790c90001dc23000000036459fff2 (last access: 13 October 2022), 2021.
State Council of China: National Land Planning Outline of China (2016–2030), 4 February 2017, http://www.gov.cn/zhengce/content/2017--02/04/content_5165309.htm (last access: 13 October 2022), 2017.
State Forestry Administration of China: National Forest Management Planning (2016–2050), 28 July 2016, https://www.gov.cn/xinwen/2016-07/28/content_5095504.htm?eqid=f495541b0003bd9a00000002648fc315 (last access: 13 October 2022), 2016 (data available at: https://www.gov.cn/xinwen/2016-7/28/5095504/files/b9ac167edfd748dc8c1a256a784f40d5.pdf, last access: 13 October 2022).
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P., and Friedl, M. A.: Hierarchical mapping of annual global land cover 2001 to present: The MODIS Collection 6 Land Cover product, Remote Sens. Environ., 222, 183–194, https://doi.org/10.1016/j.rse.2018.12.013, 2019.
Tan, M. and Li, X.: Does the Green Great Wall effectively decrease dust storm intensity in China? A study based on NOAA NDVI and weather station data, Land Use Policy, 43, 42–47, https://doi.org/10.1016/j.landusepol.2014.10.017, 2015.
Tang, J., Niu, X., Wang, S., Gao, H., Wang, X., and Wu, J.: Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections, J. Geophys. Res.-Atmos., 121, 2110–2129, https://doi.org/10.1002/2015JD023977, 2016.
Tatli, H. and Dalfes, H. N.: Defining Holdridge's life zones over Turkey, Int. J. Climatol., 36, 3864–3872, https://doi.org/10.1002/joc.4600, 2016.
Thrasher, B., Wang, W., Michaelis, A., Melton, F., Lee, T., and Nemani, R.: NASA global daily downscaled projections, CMIP6, Sci. Data, 9, 262, https://doi.org/10.1038/s41597-022-01393-4, 2022.
Turner, K. E., Smith, D. M., Katavouta, A., and Williams, R. G.: Reconstructing ocean carbon storage with CMIP6 Earth system models and synthetic Argo observations, Biogeosciences, 20, 1671–1690, https://doi.org/10.5194/bg-20-1671-2023, 2023.
Ullah, K. and Shouting, G.: A diagnostic study of convective environment leading to heavy rainfall during the summer monsoon 2010 over Pakistan, Atmos. Res., 120, 226–239, https://doi.org/10.1016/j.atmosres.2012.08.021, 2013.
Varney, R. M., Chadburn, S. E., Burke, E. J., and Cox, P. M.: Evaluation of soil carbon simulation in CMIP6 Earth system models, Biogeosciences, 19, 4671–4704, https://doi.org/10.5194/bg-19-4671-2022, 2022.
Veldman, J. W., Aleman, J. C., Alvarado, S. T., Anderson, T. M., Archibald, S., Bond, W. J., Boutton, T. W., Buchmann, N., Buisson, E., Canadell, J. G., Dechoum, M. D. S., Diaz-Toribio, M. H., Durigan, G., Ewel, J. J., Fernandes, G. W., Fidelis, A., Fleischman, F., Good, S. P., Griffith, D. M., Hermann, J.-M., Hoffmann, W. A., Le Stradic, S., Lehmann, C. E. R., Mahy, G., Nerlekar, A. N., Nippert, J. B., Noss, R. F., Osborne, C. P., Overbeck, G. E., Parr, C. L., Pausas, J. G., Pennington, R. T., Perring, M. P., Putz, F. E., Ratnam, J., Sankaran, M., Schmidt, I. B., Schmitt, C. B., Silveira, F. A. O., Staver, A. C., Stevens, N., Still, C. J., Strömberg, C. A. E., Temperton, V. M., Varner, J. M., and Zaloumis, N. P.: Comment on “The global tree restoration potential”, Science, 366, eaay7976, https://doi.org/10.1126/science.aay7976, 2019.
Verbruggen, W., Schurgers, G., Horion, S., Ardö, J., Bernardino, P. N., Cappelaere, B., Demarty, J., Fensholt, R., Kergoat, L., Sibret, T., Tagesson, T., and Verbeeck, H.: Contrasting responses of woody and herbaceous vegetation to altered rainfall characteristics in the Sahel, Biogeosciences, 18, 77–93, https://doi.org/10.5194/bg-18-77-2021, 2021.
Wang, H., Zhao, W., Li, C., and Pereira, P.: Vegetation greening partly offsets the water erosion risk in China from 1999 to 2018, Geoderma, 401, 115319, https://doi.org/10.1016/j.geoderma.2021.115319, 2021.
Wang, H., Yue, C., and Luyssaert, S.: Reconciling different approaches to quantifying land surface temperature impacts of afforestation using satellite observations, Biogeosciences, 20, 75–92, https://doi.org/10.5194/bg-20-75-2023, 2023.
Wang, J. and Kotamarthi, V. R.: High-resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America, Earths Future, 3, 268–288, https://doi.org/10.1002/2015EF000304, 2015.
Wang, M., Zhang, X., and Yan, X.: Modeling the climatic effects of urbanization in the Beijing–Tianjin–Hebei metropolitan area, Theor. Appl. Climatol., 113, 377–385, https://doi.org/10.1007/s00704-012-0790-z, 2013.
Wilby, R. L. and Dawson, C. W.: The statistical downscaling model: insights from one decade of application, Int. J. Climatol., 33, 1707–1719, https://doi.org/10.1002/joc.3544, 2013.
Wu, J. and Gao, X.: A gridded daily observation dataset over China region and comparison with the other datasets, Chinese J. Geophys.-Ch., 56, 1102–1111, https://doi.org/10.6038/cjg20130406, 2013 (data available at: https://ccrc.iap.ac.cn/resource/detail?id=228, last access: 20 February 2023).
Wu, J. and Gao, X.: Present day bias and future change signal of temperature over China in a series of multi-GCM driven RCM simulations, Clim. Dynam., 54, 1113–1130, https://doi.org/10.1007/s00382-019-05047-x, 2020.
Wu, Z., Wang, X., Liu, F., Zhu Y., Li, S., Li, B., He, S., Zhang, X., Chen, C., Zhou, Y., Zhou, G., Lin, Y., and Hou, X.: China Vegetation, Science Press, Beijing, ISBN 9787030024220, 1980 (data available at: https://www.resdc.cn/data.aspx?DATAID=133, last access: 26 December 2022).
Xiao, J.: Satellite evidence for significant biophysical consequences of the “Grain for Green” Program on the Loess Plateau in China, J. Geophys. Res.-Biogeo., 119, 2261–2275, https://doi.org/10.1002/2014JG002820, 2014.
Xu, J.: Estimation of the spatial distribution of potential forestation land and its climatic potential productivity in China, Acta Geographica Sinica, 78, 677–693, https://doi.org/10.11821/dlxb202303011, 2023.
Xu, Z. and Yang, Z. L.: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations, J. Climate, 25, 6271–6286, https://doi.org/10.1175/JCLI-D-12-00005.1, 2012.
Yan, Y., Tang, J., Liu, G., and Wu, J.: Effects of vegetation fraction variation on regional climate simulation over Eastern China, Global Planet. Change, 175, 173–189, https://doi.org/10.1016/j.gloplacha.2019.02.004, 2019.
Yan, Y., Tang, J., Wang, S., Niu, X., and Wang, L.: Uncertainty of land surface model and land use data on WRF model simulations over China, Clim. Dynam., 57, 1833–1851, https://doi.org/10.1007/s00382-021-05778-w, 2021.
Yang, K., Wang, C., and Li, S.: Improved simulation of frozen-thawing process in land surface model (CLM4.5), J. Geophys. Res.-Atmos., 123, 13238–13258, https://doi.org/10.1029/2017JD028260, 2018.
Yang, X., Zhou, B., Xu, Y., and Han, Z.: CMIP6 evaluation and projection of temperature and precipitation over China, Adv. Atmos. Sci., 38, 817–830, https://doi.org/10.1007/s00376-021-0351-4, 2021.
You, N., Meng, J., Zhu, L., Jiang, S., Zhu, L., Li, F., and Kuo, L. J.: Isolating the impacts of land use/cover change and climate change on the GPP in the Heihe River Basin of China, J. Geophys. Res.-Biogeo., 125, e2020JG005734, https://doi.org/10.1029/2020JG005734, 2020.
Yu, E., Sun, J., Chen, H., and Xiang, W.: Evaluation of a high-resolution historical simulation over China: climatology and extremes, Clim. Dynam., 45, 2013–2031, https://doi.org/10.1007/s00382-014-2452-6, 2015.
Yu, L., Liu, T., Bu, K., Yang, J., Chang, L., and Zhang, S.: Influence of snow cover changes on surface radiation and heat balance based on the WRF model, Theor. Appl. Climatol., 130, 205–215, https://doi.org/10.1007/s00704-016-1856-0, 2017.
Yu, L., Liu, Y., Liu, T., and Yan, F.: Impact of recent vegetation greening on temperature and precipitation over China, Agr. Forest. Meteorol., 295, 108197, https://doi.org/10.1016/j.agrformet.2020.108197, 2020.
Yu, Z., Ciais, P., Piao, S., Houghton, R. A., Lu, C., Tian, H., Agathokleous, E., Kattel, G. R., Sitch, S., Goll, D., Yue, X., Walker, A., Friedlingstein, P., Jain, A. K., Liu, S., and Zhou, G.: Forest expansion dominates China's land carbon sink since 1980, Nat. Commun., 13, 5374, https://doi.org/10.1038/s41467-022-32961-2, 2022.
Yuan, G., Tang, W., Zuo, T., Li, E., Zhang, L., and Liu, Y.: Impacts of afforestation on land surface temperature in different regions of China, Agr. Forest. Meteorol., 318, 108901, https://doi.org/10.1016/j.agrformet.2022.108901, 2022.
Zevallos, J. and Lavado-Casimiro, W.: Climate change impact on Peruvian biomes, Forests, 13, 238, https://doi.org/10.3390/f13020238, 2022.
Zhang, L., Sun, P., Huettmann, F., and Liu, S.: Where should China practice forestry in a warming world?, Glob. Change Biol., 28, 2461–2475, https://doi.org/10.1111/gcb.16065, 2022.
Zhang, P., Shao, G., Zhao, G., Le Master, D. C., Parker, G. R., Dunning Jr., J. B., and Li, Q.: China's forest policy for the 21st century, Science, 288, 2135–2136, https://doi.org/10.1126/science.288.5474.2135, 2000.
Zhang, X., Ding, N., Han, S., and Tang, Q.: Irrigation-induced potential evapotranspiration decrease in the Heihe River Basin, Northwest China, as simulated by the WRF model, J. Geophys. Res.-Atmos., 125, e2019JD031058, https://doi.org/10.1029/2019JD031058, 2020.
Zhang, X., Chen, J., and Song, S.: Divergent impacts of land use/cover change on summer precipitation in eastern China from 1980 to 2000, Int. J. Climatol., 41, 2360–2374, https://doi.org/10.1002/joc.6963, 2021.
Zhang, Y. and Song, C.: Impacts of afforestation, deforestation, and reforestation on forest cover in China from 1949 to 2003, J. Forest., 104, 383–387, https://doi.org/10.1093/jof/104.7.383, 2006.
Zhao, D., Lin, Y., Dong, W., Qin, Y., Chu, W., Yang, K., Letu, H., and Huang, L.: Alleviated WRF summer wet bias over the Tibetan Plateau using a new cloud macrophysics scheme, J. Adv. Model. Earth Sy., 15, e2023MS003616, https://doi.org/10.1029/2023MS003616, 2023.
Zhao, X., Ma, X., Chen, B., Shang, Y., and Song, M.: Challenges toward carbon neutrality in China: Strategies and countermeasures, Resour. Conserv. Recy., 176, 105959, https://doi.org/10.1016/j.resconrec.2021.105959, 2022.
Zhao, Y., Zhong, L., Ma, Y., Fu, Y., Chen, M., Ma, W., Zhao, C., Huang, Z., and Zhou, K.: WRF/UCM simulations of the impacts of urban expansion and future climate change on atmospheric thermal environment in a Chinese megacity, Climatic Change, 169, 38, https://doi.org/10.1007/s10584-021-03287-7, 2021.
Zheng, Y., Alapaty, K., Herwehe, J. A., Del Genio, A. D., and Niyogi, D.: Improving high-resolution weather forecasts using the Weather Research and Forecasting (WRF) Model with an updated Kain–Fritsch scheme, Mon. Weather Rev., 144, 833–860, https://doi.org/10.1175/MWR-D-15-0005.1, 2016.
Zhu, K., Song, Y., and Qin, C.: Forest age improves understanding of the global carbon sink, P. Natl. Acad. Sci. USA, 116, 3962–3964, https://doi.org/10.1073/pnas.1900797116, 2019.
Zomer, R. J., Trabucco, A., Bossio, D. A., and Verchot, L. V.: Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation, Agr. Ecosyst. Environ., 126, 67–80, https://doi.org/10.1016/j.agee.2008.01.014, 2008.
Zuo, Z., Fung, J. C., Li, Z., Huang, Y., Wong, M. F., Lau, A. K., and Lu, X.: Projection of future heatwaves in the Pearl River Delta through CMIP6-WRF dynamical downscaling, J. Appl. Meteorol. Clim., 62, 1297–1314, https://doi.org/10.1175/JAMC-D-22-0201.1, 2023.
Short summary
We mapped the distribution of future potential afforestation regions based on future high-resolution climate data and climate–vegetation models. After considering the national afforestation policy and climate change, we found that the future potential afforestation region was mainly located around and to the east of the Hu Line. This study provides a dataset for exploring the effects of future afforestation.
We mapped the distribution of future potential afforestation regions based on future...
Altmetrics
Final-revised paper
Preprint