Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO&lt;sub&gt;2&lt;/sub&gt;

Abstract. Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

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Slow-down of the greening trend in natural vegetation with further rise in atmospheric CO<sub>2</sub>

10.5194/bg-2021-37 ◽

2021 ◽

Author(s):

Alexander J. Winkler ◽

Ranga B. Myneni ◽

Alexis Hannart ◽

Stephen Sitch ◽

Vanessa Haverd ◽

...

Keyword(s):

Climate Change ◽

Leaf Area ◽

Carbon Emissions ◽

Land Surface ◽

Scale Effects ◽

Natural Vegetation ◽

Climate Projections ◽

Co2 Fertilization ◽

Area Index ◽

Anthropogenic Carbon

Abstract. Satellite data reveal widespread changes of Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change and consequent disturbances, such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at biome-level that can be scaled into a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies of vegetation changes, and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying Causal Counterfactual Theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). Our results do not support previously published accounts of dominant global-scale effects of CO2 fertilization. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last two decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slow-down in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

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Slow-down of the greening trend in natural vegetation with further rise in atmospheric CO2

10.5194/egusphere-egu2020-8066 ◽

2020 ◽

Author(s):

Alexander J. Winkler ◽

Ranga B. Myneni ◽

Alexis Hannart ◽

Victor Brovkin

Keyword(s):

Climate Change ◽

Leaf Area ◽

Land Surface ◽

Scale Effects ◽

Carbon Sink ◽

Global Scale ◽

Natural Vegetation ◽

Earth System ◽

Area Index ◽

Probabilistic Setting

<div> <div> <div> <p>Satellite data reveal widespread changes in vegetation cover of Earth&#8217;s land surfaces. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO<sub>2 </sub>fertilization, climate change and consequent episodic disturbances (<em>e.g. </em>fires and droughts) are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at biome-level that can be scaled into a global picture of what is behind the observed changes is currently lacking.</p> <p>Therefore, we analyze here the longest available satellite record of global leaf area index (LAI, 1981-2017) and identify several clusters of significant long-term changes at the biome scale. Using process-based model simulations (fully-coupled MPI-M Earth system model and 13 stand-alone land surface models), we disentangle the effects of rising CO<sub>2 </sub>on LAI in a probabilistic setting applying Causal Counterfactual Theory.</p> <p>Our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last two decades (2000-2017). The decreases in LAI are primarily concentrated in regions of high LAI (<em>i.e. </em>tropical forests), whereas the increases are in low LAI regions (<em>i.e. </em>northern and arid lands). These opposing trends are reducing the LAI texture of natural vegetation at the global scale. The analysis prominently indicates the effects of climate change on many biomes &#8211; warming in northern ecosystems and rainfall anomalies in tropical biomes. Our results do not support previously published accounts of dominant global-scale effects of CO<sub>2 </sub>fertilization. Most models largely underestimate vegetation browning, especially in the tropical rainforests. The leaf area loss in these productive ecosystems could be an early indicator of a slow-down in the terrestrial carbon sink. Models need to better account for this effect to realize plausible Earth system projections of the 21<sup>st </sup>century.</p> </div> </div> </div>

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Potential natural vegetation dynamics driven by future long-term climate change and its hydrological impacts in the Hanjiang River basin, China

Hydrology Research ◽

10.2166/nh.2011.111 ◽

2012 ◽

Vol 43 (1-2) ◽

pp. 73-90 ◽

Cited By ~ 7

Author(s):

Fei Yuan ◽

Liliang Ren ◽

Zhongbo Yu ◽

Yonghua Zhu ◽

Jing Xu ◽

...

Keyword(s):

Climate Change ◽

River Basin ◽

21St Century ◽

Land Surface ◽

Natural Vegetation ◽

Potential Natural Vegetation ◽

Area Index ◽

Hanjiang River ◽

Ipcc Sres

Vegetation and land-surface hydrology are intrinsically linked under long-term climate change. This paper aims to evaluate the dynamics of potential natural vegetation arising from 21st century climate change and its possible impact on the water budget of the Hanjiang River basin in China. Based on predictions of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC-SRES) A1 scenario from the PRECIS (Providing Regional Climates for Impact Studies) regional climate model, changes in plant functional types (PFTs) and leaf area index (LAI) were simulated via the Lund-Potsdam-Jena dynamic global vegetation model. Subsequently, predicted PFTs and LAIs were employed in the Xinanjiang vegetation-hydrology model for rainfall–runoff simulations. Results reveal that future long-term changes in precipitation, air temperature and atmospheric CO2 concentration would remarkably affect the spatiotemporal distribution of PFTs and LAIs. These climate-driven vegetation changes would further influence regional water balance. With the decrease in forest cover in the 21st century, plant transpiration and evaporative loss of intercepted canopy water will tend to fall while soil evaporation may rise considerably. As a result, total evapotranspiration may increase moderately with a slight increase in annual runoff depth. This indicates that, for long-term hydrological prediction, climate-induced changes in terrestrial vegetation cannot be neglected as the terrestrial biosphere plays an important role in land-surface hydrological responses.

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Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Remote Sensing of Environment ◽

10.1016/j.rse.2011.01.001 ◽

2011 ◽

Vol 115 (5) ◽

pp. 1171-1187 ◽

Cited By ~ 168

Author(s):

Hua Yuan ◽

Yongjiu Dai ◽

Zhiqiang Xiao ◽

Duoying Ji ◽

Wei Shangguan

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Climate Modelling ◽

Area Index

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Impact of leaf area index seasonality on the annual land surface evaporation in a global circulation model

Journal of Geophysical Research Atmospheres ◽

10.1029/2002jd002846 ◽

2003 ◽

Vol 108 (D6) ◽

Cited By ~ 66

Author(s):

Bart J. J. M. van den Hurk

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Circulation Model ◽

Global Circulation Model ◽

Area Index ◽

Surface Evaporation ◽

Global Circulation

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Effects of Forest Changes on Summer Surface Temperature in Changbai Mountain, China

Forests ◽

10.3390/f12111551 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1551

Author(s):

Jiaqi Zhang ◽

Xiangjin Shen ◽

Yanji Wang ◽

Ming Jiang ◽

Xianguo Lu

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface ◽

Climate Models ◽

Regional Climate ◽

Changbai Mountain ◽

Vegetation Coverage ◽

Area Index ◽

Forest Coverage ◽

Cooling Effects

The area and vegetation coverage of forests in Changbai Mountain of China have changed significantly during the past decades. Understanding the effects of forests and forest coverage change on regional climate is important for predicting climate change in Changbai Mountain. Based on the satellite-derived land surface temperature (LST), albedo, evapotranspiration, leaf area index, and land-use data, this study analyzed the influences of forests and forest coverage changes on summer LST in Changbai Mountain. Results showed that the area and vegetation coverage of forests increased in Changbai Mountain from 2003 to 2017. Compared with open land, forests could decrease the summer daytime LST (LSTD) and nighttime LST (LSTN) by 1.10 °C and 0.07 °C, respectively. The increase in forest coverage could decrease the summer LSTD and LSTN by 0.66 °C and 0.04 °C, respectively. The forests and increasing forest coverage had cooling effects on summer temperature, mainly by decreasing daytime temperature in Changbai Mountain. The daytime cooling effect is mainly related to the increased latent heat flux caused by increasing evapotranspiration. Our results suggest that the effects of forest coverage change on climate should be considered in climate models for accurately simulating regional climate change in Changbai Mountain of China.

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Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

The Forestry Chronicle ◽

10.5558/tfc82159-2 ◽

2006 ◽

Vol 82 (2) ◽

pp. 159-176 ◽

Cited By ~ 16

Author(s):

R J Hall ◽

F. Raulier ◽

D T Price ◽

E. Arsenault ◽

P Y Bernier ◽

...

Keyword(s):

Climate Change ◽

Remote Sensing ◽

Leaf Area Index ◽

Leaf Area ◽

Management Practices ◽

Forest Productivity ◽

Process Models ◽

Growth And Yield ◽

Area Index ◽

Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

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An Improved Canopy Integration Scheme for a Land Surface Model with Prognostic Canopy Structure

Journal of Climate ◽

10.1175/jcli4222.1 ◽

2007 ◽

Vol 20 (15) ◽

pp. 3902-3923 ◽

Cited By ~ 147

Author(s):

Peter E. Thornton ◽

Niklaus E. Zimmermann

Keyword(s):

Leaf Area ◽

Specific Leaf Area ◽

Land Surface ◽

Temperate Climate ◽

Integration Scheme ◽

Surface Model ◽

Area Index ◽

Climate System Model ◽

Community Land Model ◽

Canopy Model

Abstract A new logical framework relating the structural and functional characteristics of a vegetation canopy is presented, based on the hypothesis that the ratio of leaf area to leaf mass (specific leaf area) varies linearly with overlying leaf area index within the canopy. Measurements of vertical gradients in specific leaf area and leaf carbon:nitrogen ratio for five species (two deciduous and three evergreen) in a temperate climate support this hypothesis. This new logic is combined with a two-leaf (sunlit and shaded) canopy model to arrive at a new canopy integration scheme for use in the land surface component of a climate system model. An inconsistency in the released model radiation code is identified and corrected. Also introduced here is a prognostic canopy model with coupled carbon and nitrogen cycle dynamics. The new scheme is implemented within the Community Land Model and tested in both diagnostic and prognostic canopy modes. The new scheme increases global gross primary production by 66% (from 65 to 108 Pg carbon yr−1) for diagnostic model simulations driven with reanalysis surface weather, with similar results (117 PgC yr−1) for the new prognostic model. Comparison of model predictions to global syntheses of observations shows generally good agreement for net primary productivity (NPP) across a range of vegetation types, with likely underestimation of NPP in tundra and larch communities. Vegetation carbon stocks are higher than observed in forest systems, but the ranking of stocks by vegetation type is accurately captured.

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