Displays

People have increasingly been shaping the surface of our planet. Land use/cover change – the most visible human footprint on Earth – is one of the main contributors to greenhouse gas emissions and biodiversity loss and, hence, is a key topic for current sustainability debates and climate change mitigation. To understand these land surface dynamics and its impacts, accurate reconstructions of global land use/cover change are necessary. Although more and more observational data sets are publicly available (e.g. from remote sensing), current land change assessments are still incomplete and either lack temporal consistency, spatial explicitness or thematic detail. Here, we show a consistent reconstruction of global land use/cover change from 1960-2015, using an open data-driven approach that combines national land use statistics with earth observation data of multiple sources and scales. Our land change reconstruction model HILDA+ (Historic Land Dynamics Assessment) accounts for data-derived gross changes within six main land use/cover classes at 1 km spatial resolution: Urban areas, cropland, pastures and rangeland, forest, (semi-)natural grass- or shrubland, other land. As a result, we present yearly land use/cover maps at 1 km spatial resolution, magnitudes and hot spot areas of change. Globally, around 20 % of the land surface – almost three times the size of Brazil - has undergone change within the last 55 years. Further, gross change is about seven times as high as yearly net change extent for forest, cropland and pasture dynamics. We prove that land change studies accounting for net change only can lead to severe underestimations of change extent and frequency. With this purely data-driven approach, we address current research needs of the earth system modelling community by providing new layers of land use/cover change with unprecedented level of detail. Learning from the recent past, understanding how management and land cover dynamics interactively affect the climate is essential for implementing measures of mitigation and sustainable land use policies. In this context, a solid information base can support informed decision-making.

How to cite: Winkler, K., Fuchs, R., Herold, M., and Rounsevell, M.: What open data tells us - Reconstructing 55 years of global land use/cover change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18199, https://doi.org/10.5194/egusphere-egu2020-18199, 2020.

Global land models, which often served as components Earth system models, and national GHG inventories rely on different methods and produce different estimates of anthropogenic CO₂ emissions and uptakes from land use land cover changes throughout historical period. For example, for 2005 -2014, the sum of the national GHG inventories net emission estimates is 0.1 ± 1.0 GtCO2 yr^–1 while the bookkeeping models is 5.2 ± 2.6 GtCO2 yr^–1 (IPCC SPM 2019).  Previous estimates with the 16 global stand-alone land models produced an estimate of the net land sink of 11.2 ± 2.6 GtCO2 yr^–1 during 2007– 2016 for the natural response of land to human-induced environmental changes such as increasing atmospheric CO₂ concentration, nitrogen deposition, and climate change (IPCC SPM 2019).  However, these 16 models do not provide separate estimates for the managed and unmanaged lands. 

Here we use results from simulations with the NOAA/GFDL new land model LM4.1 from the CMIP6 Land Use Model Inercomparison Project (LUMIP) to demonstrate how to reconcile the discrepancy between the inventories and land models estimates of the anthropogenic CO₂ land emissions by using bookkeeping accounting approach applied to the model results.  In addition, we separate estimates of land fluxes on managed and unmanaged lands. Key features of this model include advanced, second generation dynamic vegetation representation and canopy competition, fire, and land use representation driven by full set of gross transitions from the CMIP6 land use scenarios.  We demonstrate how bookkeeping accounting combined with the LUMIP experiments can enhance understanding of land sector net emission estimates and their applications.

How to cite: Shevliakova, E., Malyshev, S., Houghton, R., and Verchot, L.: Reconciling global land model estimates and country reporting of anthropogenic land CO2 sources and sinks with the CMIP6 LUMIP NOAA/GFDL LM4.1 simulations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20310, https://doi.org/10.5194/egusphere-egu2020-20310, 2020.

Assessment of the plant-climatic interactions in the land biosphere requires a combined perspective of both the biogeochemical effects (BGC; such as the carbon sink), and the biogeophysical effects (BGP; such as the vegetation albedo and radiative balance), which can often have contrasting consequences for ecosystem functioning and climate. Aiming to increase our knowledge on semi-arid ecosystems that are insufficiently represented in global studies, we examine the variations in key BGP features among different vegetation types in a dry Mediterranean region in southern Israel.

The study included planted pine forest (pinus halepensis), natural broad-leaf oak maquis (Quercus calliprinos), wheat field and a managed grassland, located in close proximity (within 2 km) under the same climatic conditions (mean annual temperature = 20.8C, annual mean precipitation, P= 403 mm, aridity index = 0.4). Using a state-of-the-art mobile laboratory, we carried out measurement campaigns of eddy covariance fluxes of CO2, sensible, H, and latent, LE, heat fluxes, and the radiation balance (incoming and outgoing short- and long-wave radiations) between the ecosystems and the atmosphere in different seasons during 2016-2018.

The results showed significant differences in net radiation and in albedo among the ecosystem, with net radiation values of ~666, ~582, ~443 and 456 W m^-2 and albedo values of ~0.13, ~0.16, ~0.19 and ~0.20 for pines, maquis, wheat and grassland, respectively. The lowest albedo of the pine stand was associated with the largest H (a ‘convector effect’) of ~583 W m^-2 compared to ~313, ~198 and ~176 W m^-2 in the maquis, wheat and grassland ecosystems (midday means of peak activity season). The pine stand was also more adjusted to stress conditions than the oak maquis ecosystem through ‘avoidance’ of high activities during extreme conditions of heat and drought (reducing canopy conductance and associated fluxes). It is likely that the observed differences between the pine and oak maquis stand help explain the greater expansion of pine stands into the semi-arid regions, even to areas with mean annual P of 290 mm (aridity index = 0.2) where oak maquis cannot be found.

How to cite: Amer, M., Stern, R., Rotenberg, E., and Yakir, D.: Variation in canopy energy exchange characteristics across an ecosystem mosaic in the dry Mediterranean region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21654, https://doi.org/10.5194/egusphere-egu2020-21654, 2020.

This study aims at better understanding the impact of deforestation on the local hydrology over the Argentinian Chaco, using land surface modeling and remote sensing data. The Chaco is an ecoregion characterized by unprecedented deforestation since the 1980s, mainly for cattle ranging and soybean production. More specifically, default climatological vegetation parameters (LAI, GVF) and static land cover in state-of-the-art land surface models (LSM), grouped within the NASA Land Information System (LIS), are updated using satellite-based dynamic vegetation parameters and yearly land use maps to feed the models with deforestation.

The presentation will show a spatio-temporal analysis of long-term water budget simulations using a range of LSMs (Noah, CLM, CLSM) in which dynamically updated vegetation and land cover parameters are included. Our simulations indicate that different LSMs result in a different partitioning of the total water budget, but all indicate an increase in soil moisture and percolation over the deforested areas.  Model output is evaluated using in situ soil moisture data, and various soil moisture retrieval products from SMOS (operational Level 2 and SMOS-IC) and SMAP (operational Level 2) and evapotranspiration data from GLEAM.

How to cite: Maertens, M., De Lannoy, G., Apers, S., and Kumar, S.: Revealing the impact of deforestation on hydrology using remote sensing and land surface modeling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9612, https://doi.org/10.5194/egusphere-egu2020-9612, 2020.

Limiting global warming to less than 2°C relative to preindustrial times by the end of this century requires a rapid and long-lasting decarbonization. In contrast to the other major renewable energy sources, solar and wind, hydropower reservoirs allow storing energy and releasing it when required, a significant advantage for stabilizing electrical grids. The establishment of hydropower reservoirs typically involves a land-use change when formerly terrestrial ecosystems are inundated. One, hitherto overlooked, consequence of this land-use change is a decrease in surface albedo, as waterbodies are characterized by a lower albedo compared to most terrestrial ecosystems. The main objective of this study is to quantify the positive radiative forcing resulting from this albedo change and to oppose it with the negative radiative forcing resulting from the fossil fuel displacement by the hydropower electricity generation. To that end, we compiled, on the basis of publicly available datasets, a global database of hydropower reservoirs. The hypothetical change in albedo associated with their construction was assessed on the basis of the difference in remotely sensed albedo (MODIS MCD43A1) between the hydropower reservoir and the surrounding landscape. We then calculated the break-even point, that is the time required for the time-integrated negative radiative resulting from the fossil fuel displacement to offset the positive radiative forcing from the albedo difference. The major result from this study is that break-even times range from less than a year up to several years and even a few decades. The key metric governing these differences is the annual electricity generation to reservoir surface area ratio, low ratios resulting in unfavorably long break-even times. Additional influence factors having a modulating influence are latitude, governing the incident solar radiation, and the magnitude of the albedo difference. We conclude that the displacement of fossil fuels by hydropower wins over the albedo penalty in the long-term. In the short-term, and thus for contributing towards the goal of a rapid decarbonization, the albedo penalty may be dominating and needs to be considered in the design of hydropower plants.

How to cite: Hammerle, A., Tomelleri, E., and Wohlfahrt, G.: The albedo-climate penalty of hydropower, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19483, https://doi.org/10.5194/egusphere-egu2020-19483, 2020.

Beyond global mean temperatures, anthropogenic land cover change (LCC) can have significant impacts at regional and seasonal scales but also on extreme weather events to which human, natural and economical systems are highly vulnerable. However, the effects of LCC on extreme events remain either largely unexplored at global and regional scale and/or without consensus. Here, using several Earth System Models under two different LCC scenarios (the RCP8.5 and RCP2.6 Representative Concentration Pathways) and analyzing 20 extreme weather indices, we find future LCC substantially modulates projected weather extremes particularly at regional level.

On average by the end of the 21^st century, under RCP8.5 and RCP2.6 scenarios, future LCC robustly lessens global projections of high rainfall extremes. Accounting for LCC diminishes regional projections of heavy precipitation days or consecutive wet days by more than 50% in southern Africa or northeastern Brazil but intensifies projected dry days in eastern Africa by 30%. LCC do not substantially affect projections of global and regional temperature extremes projections (<5%), but it can impact global rainfall extremes 2.5 times more than global mean rainfall projections.

Under RCP2.6 scenario, global LCC impacts are similar but of lesser magnitude while at regional scale in Amazon or Asia, LCC enhances drought projections. We investigate the underlying biophysical drivers behind those projected changes.

We stress here that multi-coupled modelling frameworks incorporating all aspects of land use-land cover change and more model-data benchmarking are needed for reliable projections of extreme events.

How to cite: Quesada, B. and Sy, S.: Anthropogenic land cover change impact on climate extremes during the 21st century, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8595, https://doi.org/10.5194/egusphere-egu2020-8595, 2020.

Afforestation is considered a cost-effective and readily available climate change mitigation option. In recent studies afforestation is presented as a major solution to limit climate change. However, estimates of afforestation potential vary widely. Moreover, the risks in global mitigation policy and the negative trade-offs with food security are often not considered. Here, we present a new approach to assess the economic potential of afforestation with the IMAGE 3.0 integrated assessment model framework (Doelman et al., 2019). In addition, we discuss the role of afforestation in mitigation pathways and the effects of afforestation on the food system under increasingly ambitious climate targets. We show that afforestation has a mitigation potential of 4.9 GtCO₂/yr at 200 US$/tCO₂ in 2050 leading to large-scale application in an SSP2 scenario aiming for 2°C (410 GtCO₂ cumulative up to 2100). Afforestation reduces the overall costs of mitigation policy. However, it may lead to lower mitigation ambition and lock-in situations in other sectors. Moreover, it bears risks to implementation and permanence as the negative emissions are increasingly located in regions with high investment risks and weak governance, for example in Sub-Saharan Africa. Our results confirm that afforestation has substantial potential for mitigation. At the same time, we highlight that major risks and trade-offs are involved. Pathways aiming to limit climate change to 2°C or even 1.5°C need to minimize these risks and trade-offs in order to achieve mitigation sustainably.

The afforestation study published as Doelman et al. (2019) excluded biophysical climate effects of land use and land cover change on climate, even though this is shown to have a substantial effect especially locally (Alkama & Cescatti, 2016). As a follow-up to this study we implement the grid-specific temperature effects as derived by Duveiller et al. (2020) to the mitigation scenarios with large-scale afforestation to assess the effectiveness of afforestation for climate change mitigation as increased or reduced effectiveness may change cost-optimal climate policy. Notably in the boreal regions this can have a major effect, as transitions from agricultural land to forest are shown to have a substantial warming effect due to reduced albedo limiting the mitigation potential in these regions. Conversely, in the tropical areas the already high mitigation potential of afforestation could be even more efficient, as increased evapotranspiration from forests leads to additional cooling. However, it is uncertain whether the high efficiency of afforestation in tropical regions can be utilized as these are also the regions with high risks to implementation and permanence.

References

Alkama, R., & Cescatti, A. (2016). Biophysical climate impacts of recent changes in global forest cover. Science, 351(6273), 600-604.

Doelman, J. C., Stehfest, E., van Vuuren, D. P., Tabeau, A., Hof, A. F., Braakhekke, M. C., . . . Lucas, P. L. (2019). Afforestation for climate change mitigation: Potentials, risks and trade-offs. Global Change Biology

Duveiller, G., Caporaso, L., Abad-Viñas, R., Perugini, L., Grassi, G., Arneth, A., & Cescatti, A. (2020). Local biophysical effects of land use and land cover change: towards an assessment tool for policy makers. Land Use Policy, 91, 104382. 

How to cite: Doelman, J., Stehfest, E., van Vuuren, D., Tabeau, A., Hof, A., Braakhekke, M., Gernaat, D., van den Berg, M., van Zeist, W.-J., Daioglou, V., van Meijl, H., and Lucas, P.: Afforestation for climate change mitigation: Potentials, risks and trade-offs, and the role of biophysical climate effects, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18838, https://doi.org/10.5194/egusphere-egu2020-18838, 2020.

We present first results of idealized deforestation experiment designed within the Land Use Model Intercomparison Project (LUMIP). In order to obtain a robust signal-to-noise ratio and to harmonize deforestation implementation across participating ESMs, global forest extent is linearly decreased by 20 million km² for the 30% of most forested grid cells over a period of 50 years starting from pre-industrial climate conditions. This experimental setup is in favor of predominantly tropical deforestation patterns, however, there is also substantial boreal deforestation. In this experiment, atmospheric and oceanic physical processes respond to large-scale deforestation while other forcings such as atmospheric CO₂ concentration and aerosol load are kept constant at the pre-industrial level.

First analysis of results from ESMs participating in the LUMIP experiments reveal a general cooling trend in response to deforestation, although a spread in an amplitude of response is substantial. In boreal region there is significant cooling effect, presumably due to an increase in surface albedo, while tropical deforestation results in a regional warming in most of models. A sensitivity of temperature change per forest fraction change on a grid cell level, ∂T/∂F, likely could be used as a generic response for any forest change scenario, although it is complicated by mixing together local and non-local effects. We also quantified so-called “zero effect latitude” at which forest cover change does not have pronounced biogeophysical effect. It is located in northern subtropics in most models.

Analyses of ensemble-members of three models (MPI-ESM1.2-LR, IPSL-CM6A-LR, and CESM2) indicate that the “time of emergence” of climate response, when signal becomes larger than a noise, is quite different among the models. However, when we compare the “deforested fraction of emergence”, the model responses become much more coherent. Biomass and soil carbon storages are decreasing with time, and their “time of emergence” is much shorter comparing to the temperature and precipitation. More results of biogeophysical and biogeochemical responses to deforestation will be presented.

How to cite: Brovkin, V., Boysen, L., Pongratz, J., Vuichard, N., Peylin, P., and Lawrence, D.: Model intercomparison of idealized global deforestation experiments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10295, https://doi.org/10.5194/egusphere-egu2020-10295, 2020.

The effects of increasing CO2 concentrations on plant and carbon cycle have been extensively investigated; however, the effects of changes in plants on the hydrological cycle are still not fully understood. Increases in CO2 modify the stomatal conductance and water use of plants, which may have a considerable effect on the hydrological cycle. Using the carbon–climate feedback experiments from CMIP5, we estimated the responses of plants and hydrological cycle to rising CO2 concentrations to double of pre-industrial levels without climate change forcing. The mode results show that rising CO2 concentrations had a significant influence on the hydrological cycle by changing the evaporation and transpiration of plants and soils. The increases in the area covered by plant leaves result in the increases in vegetation evaporation. Besides, the physiological effects of stomatal closure were stronger than the opposite effects of changes in plant structure caused by the increases in LAI (leaf area index), which results in the decrease of transpiration. These two processes lead to overall decreases in evaporation, and then contribute to increases in soil moisture and total runoff. In the dry areas, the stronger increase in LAI caused the stronger increases in vegetation evaporation and then lead to the overall decreases in P − E (precipitation minus evaporation) and soil moisture. However, the soil moisture in sub-arid and wet areas would increase, and this may lead to the soil moisture deficit worse in the future in the dry areas. This study highlights the need to consider the different responses of plants and the hydrological cycle to rising CO2 in dry and wet areas in future water resources management, especially in water-limited areas.

How to cite: Hong, T. and Ji, D.: The response of vegetation to rising CO2 concentrations plays an important role in future changes in the hydrological cycle, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18586, https://doi.org/10.5194/egusphere-egu2020-18586, 2020.

According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2°C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth’s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions.  Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275° horizontal resolution over Europe for the time period from 1992 to 2015.

Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.

We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.

Acknowledgement:

1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.

2: Appreciation for the support of Jürg Luterbacher and Eva Nowatzki.

How to cite: Zhang, M., Helmert, J., and Tölle, M.: Impact of different and time-varying land cover data sets in a regional climate model on regional and local climate over Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-631, https://doi.org/10.5194/egusphere-egu2020-631, 2020.

Afforestation is expected to take on a key role in the fight against climate change. A quarter of the emission reductions pledged by countries under the Paris Agreement are to be provided by newly established forests. Low emission scenarios equally rely heavily on land-based mitigation options to remove hundreds of gigatons of carbon dioxide from the atmosphere within this century. This proposed extensive change of the terrestrial land-cover will exert a biogeophysical (BGP) impact on climate by altering the surface albedo as well as the evapotranspiration capacity. These BGP processes are mostly absent in the land-use components of Integrated Assessment Models which currently only focus on carbon sequestration. Hence, their afforestation prospect does not take into account the local BGP induced cooling or warming that either enhances or counteracts the mitigation effort. Neglecting BGP processes can lead to under- or overestimating the benefits of afforestation depending on the location of the forest. In the worst case it even risks proposing afforestation in regions where new forests would warm the climate. We incorporate observation-based estimates of the BGP effect of afforestation into the land-use model MAgPIE (Model of Agricultural Production and its Impact on the Environment). MAgPIE is a land-use optimization model driven by the cost of agricultural production. It produces cost optimal land-use patterns for a set of climatic (Representative Concentration Pathways) and societal (Shared Socioeconomic Pathways) developments and has already been used to investigate afforestation and forest protection as mitigation options. We translate the BGP induced local cooling or warming to a carbon equivalent metric by using the local climate sensitivity and add it to the mitigation benefit of carbon sequestration. The mitigation incentive for afforestation will be enhanced or reduced by considering the local cooling or warming. Hence, the model will be able to endogenously judge afforestation decisions regarding both their carbon sequestration potential and BGP impact. We will report the changes in afforestation patterns imposed by considering their combined BGP and biogeochemical effects.

How to cite: Windisch, M., Humpenöder, F., and Popp, A.: Adapting afforestation patterns considering their local biogeophysical induced cooling and warming, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9237, https://doi.org/10.5194/egusphere-egu2020-9237, 2020.

Land-Use and Land Cover Changes (LULCC) play a fundamental role in land-atmosphere interactions, since they mainly regulate the exchange of latent and sensible heat between the ground and the upper air, while they control the amount of shortwave radiation absorbed by the ground. In this study, we make an attempt to investigate the biogeophysical effects of extreme land cover changes on soil variables, such as soil temperature and soil moisture. In particular, we analyze a multi-model ensemble of nine different regional climate model simulations, which had been performed over the Euro-CORDEX domain in the frame of the WCRP CORDEX Flagship Pilot Study LUCAS (Land Use and Climate Across Scales). We compare two idealized experiments: a maximally forested (called FOREST) and a fully grassed Europe (called GRASS). According to our results, the soil temperature response to forestation varies among the climate models. They show a profound seasonality and dependence by latitude. In winter, the magnitude of soil temperature changes is considered weak, showing a warming in high latitudes (around +1^oC on average) and a weak cooling over the Mediterranean region. During the summertime, in contrast, soil temperatures are higher in the GRASS experiment, especially in Central and Southern Europe (ranging from +1^oC to +3^oC depending to the model), underlying the essential role of soil moisture in determining the land-atmosphere feedbacks during the summer. In our contribution, we will present in detail the role of forest and grass characteristics and its effects on seasonal soil conditions across Europe[DR1] .  

How to cite: Sofiadis, G., Katragkou, E., Davin, E., Meier, R., Rechid, D., Hoffmann, P., Strada, S., Warrach-Sagi, K., Jach, L., Soares, P., Lima, D., Cardoso, R. M., Tolle, M., Breil, M., and Standberg, G.: Forestation effects on soil temperature across the European continent., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3119, https://doi.org/10.5194/egusphere-egu2020-3119, 2020.

Forests cover changes impact regional and global climate by altering surface roughness, albedo, and evapotranspiration. While previous research mainly focused on the impact on temperature, there has been evidence of cloud enhancement over forests at the regional level. However, how forests affect cloud cover at a global scale is unclear. In this paper, we utilized long-term cloud data from MODIS in junction with other satellite data sources to investigate the effects of forests on cloud cover in boreal summer months across the globe. Results show that forests either increase or decrease cloud cover depending on the region and such effect exhibits considerable spatial heterogeneity. We found that forests in the southern edge of tropical Amazon decreased cloud cover as much as 6%. In contrast, forests can significantly increase cloud coverage in southern part of China in temperate region. Furthermore, the cloud increase was also observed in boreal forests but with a smaller magnitude than temperate forests. Our study provides new evidence for understanding the impact of forest cover change on cloud and water cycle.

How to cite: Li, Y., Xu, R., Teuling, A. J., and Lei, Z.: The regionally varying effects of forests on cloud cover based on satellite observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4649, https://doi.org/10.5194/egusphere-egu2020-4649, 2020.

Land cover describes the general biophysical state of the surface providing also information about other aspects of the land, such as soils and water. Changes in land cover may have noticeable impact on the ecosystem biodiversity, water resources, climate system and socio-economic sectors. Therefore, the need for detecting these changes is more and more imperative, especially given the emergence of unbalances caused by natural and anthropogenic driving forces like climate change, intensive agriculture and wrong land management decisions. Land cover changes are mainly represented by changes in the biophysical properties of land surface. These properties can be measured by remote sensing-derived indices representing both the vegetation and soil conditions of a given region. In this research effort, by applying a change detection technique like change vector analysis (CVA), the relationship between the dynamic changes in such indices and land cover changes in Crete Island, Greece, was assessed and mapped for the time periods of 1999–2009 and 2009–2019. Vegetation indices such as normalized difference vegetation index (NDVI) and tasseled cap greenness (TCG), and soil indices such as albedo and tasseled cap brightness (TCB), were estimated by Landsat satellite images captured in 1999, 2009 and 2019. Based on two different index combinations (NDVI–albedo and TCG–TCB), CVA produced change results for each of the periods indicating the magnitude and type (direction) of changes, respectively. The most appropriate combination for land cover change detection in the study area was determined by an evaluation process resulting to the estimation of accuracy statistics (kappa index and overall accuracy). Although promising accuracy results were provided for both examined combinations, the change maps produced by the combination of NDVI–albedo were found to be more accurate.

Acknowledgments: This research has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology Hellas (GSRT), under Agreement No 651.

How to cite: Polykretis, C., Grillakis, M. G., and Alexakis, D. D.: Land cover change detection in Crete Island, Greece, using different combinations of biophysical indices in change vector analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4976, https://doi.org/10.5194/egusphere-egu2020-4976, 2020.

In this study, we explore plausible future states of soil organic matter, biomass, food production and soil greenhouse gas emissions across the UK under a range of climate, land use and land management scenarios. We use state-of-the-art soil biochemistry model, N14CP-Ag, combined with UKCP18 climate scenarios and ASSET land cover change and crop scenarios mapped onto a UK-wide grid with around 100,000 land parcels. Historic runs cover the period from the start of the Holocene interglacial (-12 kyr BP) to 2015; scenarios run from 2016 out to 2100. The results show variations of soil organic carbon (SOC) of around 10% between 2016 and 2100 relative to the simulated starting value of 1.4 Gton in 2015, with reductions of up to 7% under arable expansion scenarios and increases of up to 3% under grassland restoration scenarios. The effect of changing cropping patterns on UK-wide SOC is comparatively small. As climate scenarios move from lower to higher global emissions, the SOC reductions under arable expansion become more pronounced, while the SOC increases under grassland restoration diminish and eventually turn into losses. UK-wide crop yields show resilience to climate change and are maximised for the arable expansion scenario with protected sites of special scientific interest. Soil CO2 and nitrogen emissions get progressively higher in warmer climates. The results of this study are expected to contribute to a future UK agricultural policy aimed at rewarding farmers for sustainable land management practices.

How to cite: Yumashev, D., Janes-Bassett, V., and Davies, J.: The future of soil biochemistry and services in the UK under plausible climate, land use and land management scenarios, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18699, https://doi.org/10.5194/egusphere-egu2020-18699, 2020.

We aimed to estimate a nation-wide potential to improve the carbon balance of the land use sector by removing part of the current croplands on mineral soil from food and feed production to extensive grasslands or afforestation in Finland.  We combined the existing data on forest and agricultural production, and climate with predictive capacity of YASSO07 soil carbon model to estimate changes of soil carbon stock (SOC) in Finland over the past land use change (LUC) from forest to agriculture in comparison with alternative LUC or continuous agriculture in future.

The model analysis revealed that SOC loss after deforestation during the cultivation period originated mainly from the absence of woody litter input. The non-woody litter input of the forest was comparable to that of the agricultural residues thus the SOC originating from non-woody litter has not changed much during cultivation. The model estimated approximately a 30 year delay in positive soil carbon balance after the afforestation. Longer for Norway spruce than for the Pubescent birch. The comparison of two dominant tree species used for afforestation highlighted a difference in soil versus biomass carbon sequestration. The total forest biomass production and total carbon stock was larger for spruce stands than for birch stands. However, due to larger foliar and fineroot litter input birch stands sequestered more carbon into the soil than spruce stands. The analysis further revealed that extensification of cropland to grassland would not meet 4 per mill soil carbon sequestration criterion needed for achieving Paris climate CO2 reduction target and due to the spatial limitation of afforestation other management measures need to be considered e.g. adding biochar to soils for successful and more permanent CO2 offsetting.

How to cite: Tupek, B., Lehtonen, A., Mäkipää, R., Peltonen-Sainio, P., Huuskonen, S., Palosuo, T., Heikkinen, J., and Regina, K.: Land-use change and climate change mitigation potential of agricultural soils in Finland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21919, https://doi.org/10.5194/egusphere-egu2020-21919, 2020.

Changes in land use/land cover (LU/LC) practices are critical to determine and this is one of the crucial driving forces of terrestrial ecosystem productivity and carbon sink variability. However, relatively few studies have quantified the impact of LU/LC change on the terrestrial carbon cycle. In the present study, we developed a workflow for quantifying and assessing changes in terrestrial carbon stocks due to land use change using a dynamic vegetation model. The main objectives are to assess status and variation in carbon stocks across land covers, towards the quantification of spatial distribution and dynamic variation of terrestrial carbon sinks in response to LU/LC change. Here, with the CARAIB dynamic vegetation model, we perform simulations using several sets of LU/LC data to analyse the sensitivity of the carbon sink. We propose a new method of using satellite – and machine learning-based observation to reconstruct historical LU/LC change and compare it with static data from the cadastral map and dynamic data from an agent-based model coupled with CARAIB. It will quantify the spatial and temporal variability of land use during the 2000-2019 period over Belgium at high resolution. This study will give the space to analyse past information and hence calibrate the dynamic vegetation model to minimize uncertainty in the future projection (until 2035). Overall, this study allows us to understand the effect of changing land use pattern and identify the input dataset which minimizes the uncertainty in model estimation.

How to cite: Verma, A., Jacquemin, I., Francois, L., Dendoncker, N., Beckers, V., Hamdi, R., Berckmans, J., and Hallot, E.: Analysis of carbon sequestration sensitivity to recent changes in land use patterns over Belgium using a combination of models and remote sensing techniques, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9342, https://doi.org/10.5194/egusphere-egu2020-9342, 2020.

The role of Land Cover and Land Management (LCLM) changes in shaping the climate has garnered increasing interest, particularly in light of its potential for climate adaptation and mitigation. Earth System Models (ESMs), however, have hitherto handled LCLM-climate interactions as a unidirectional process, lacking explicit treatment of LCLM-Climate feedbacks. Consequences of these feedbacks nevertheless hold social relevance, affecting agricultural systems, food provision and prices. Furthermore, LCLM can be linked to extreme climate events such as heat waves and drought, which in turn carry economic costs through loss in worker productivity. It is thus essential to integrate LCLM processes and their feedbacks into ESMs, in order to build consistent storylines for future development pathways that take into account their potential for adaptation and mitigation. Moreover, to ensure robustness in the detected LCLM signals, such integration should be done over a range of ESMs.

Emulators represent a computationally cheap but effective way of approximating ESMs. Here we outline an emulator approach to represent LCLM-Climate feedbacks based on a framework developed by Beusch et al. (2019). This framework provides spatially explicit data by translating annual global mean temperatures into local temperatures and can be extended to use for other relevant variables. The emulator is developed as part of the LAnd MAnagement for CLImate Mitigation and Adaptation (LAMACLIMA) project, and is trained on dedicated ESM simulations that isolate the effects of key land management practices focussed on by LAMACLIMA: irrigation, de/reforestation and wood. Variables considered include temperature, evapotranspiration, runoff, crop yields, carbon storage and heat stress. Besides providing spatially explicit representation of these variables, the emulator also allows flexibility in prescribing land-use scenarios under which their responses are explored.

Beusch, L. Gudmundsson, and S. I. Seneviratne: Emulating Earth System Model Temperatures: from Global Mean Temperature Trajectories to Grid-point Level Realizations on Land, doi: 10.5194/esd-2019-34, 2019 (accepted for ESD).

How to cite: Nath, S., Lejeune, Q., Beusch, L., Schleussner, C., and Seneviratne, S. I.: Integrating LCLM feedbacks into climate models: an emulator approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10265, https://doi.org/10.5194/egusphere-egu2020-10265, 2020.

To evaluate the climatic and ecological impacts of anthropogenic activities in global change research, it is essential to reconstruct historical land use and land cover change on regional and global scales. In this study, we reconstructed cropland areas for 54 provinces within the European part of Tsarist Russia (ETR) over the periods of 1500-1914 using historical data, including cropland area, population, grain consumption, and grain yield per unit area. The main results are as follows. (1) Total cropland areas and fractional cropland areas of ETR for 11 time sections (1500 AD, 1540 AD, 1585 AD, 1696 AD, 1719 AD, 1725 AD, 1763 AD, 1796 AD, 1856 AD, 1887 AD and 1914 AD) during 1500-1914 were reconstructed, respectively. The total cropland area of ETR increased from 4.26×10⁴ km² in 1500 AD to 147.40×10⁴ km² in 1914 AD. The fractional cropland area increased from 2.40% to 29.20%, and the per capita cropland area decreased from 2.58 ha to 1.13 ha during 1500-1914. (2) Cropland expanded from the central and southwest of ETR to the black soil region, surrounding area of the Volga River, Ukraine region, the new Russia region, and the vicinity of Ural for the increase and migration of population. While in the northern region of ETR, cropland area remained stable due to unfavorable climatic conditions throughout the study period. (3) In 1914 AD, the cropland area and fractional cropland area of each province varied from 0.16×10⁴ km² and 0.76% to 5.65×10⁴ km² and 76.68%, respectively. (4) The comparisons show that the cropland areas on the ETR in this study for 1500-1914 are higher than those of the HYDE 3.2 dataset. The main reason might come from the underestimation of per capita cropland areas in HYDE 3.2 dataset, which values remained about 1 ha from 1500 to 1920 in that dataset.

How to cite: Zhao, Z., Fang, X., Ye, Y., Zhang, C., and Zhang, D.: Reconstruction of cropland cover changes in the European part of Tsarist Russia from 1500 to 1914 based on historical documents, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4150, https://doi.org/10.5194/egusphere-egu2020-4150, 2020.

Tropical forests play a critical role in maintaining the balance of biophysical surface fluxes and strongly influence the local and regional climate. Tropical deforestation is therefore increasingly recognised as an issue of global importance as the environmental and climatic consequences of prolific land-cover changes are beginning to be better understood. Using remotely sensed atmospheric and land-surface datasets from 2000 to 2016, climate impacts of deforestation were analysed over three tropical forest domains; the Amazon basin, the Congo basin and South-East Asia (SEA). Trends in local climate responses were observed with increasing deforestation across all tropical regions. Climate analysis was conducted on co-located pixels to ensure geographical differences were accounted for. Land that was deforested over the analysis period showed a decrease in evapotranspiration (ET) and leaf area index (LAI) and a significant increase in daytime land surface temperature (T). Whilst the Amazon saw the greatest relative decrease in LAI (0.2 m²/ m²), SEA showed the largest decrease in ET (1.5 mm/month) over the period. The climate response in Africa to deforestation is muted, with T increases of only 0.1◦C compared with 0.18◦C and 0.4◦C for SEA and the Amazon respectively. In all regions the response of precipitation was not significant. Increasing temperatures will heighten ecosystem stress for the remaining vegetation and forest adjacent to regions of deforestation will be more susceptible further degradation. The results of this study highlight the differences in climate responses between the tropical regions and the need to consider each separately when conducting future analysis.

How to cite: Smith, C., Spracklen, D., and Baker, J.: Impacts of Tropical Deforestation on Local Climate , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11712, https://doi.org/10.5194/egusphere-egu2020-11712, 2020.

Land cover and land management (LCLM) changes are important sources of anthropogenic CO₂ emissions, constituting about 10% of current annual CO2 emissions, or about one third of cumulative emissions over the industrial era. However, simulations with Earth system models (ESMs) show a large range of CO₂ emissions from LCLM. Several reasons for the divergence in estimates have been identified, in particular differences in simulated biomass and soil carbon stocks, and if and which land management practices are included in models. The divergence of model estimates is particularly worrisome since LCLM practices are discussed as key mitigation tools or “negative emission technologies” to reach the temperature goals of Paris Agreement. In the LAMACLIMA project (land management for climate mitigation and adaptation) we therefore conduct a detailed analysis of several LCLM practices across three ESMs to improve our understanding about model uncertainties. The present study aims to quantify the effects of forest cover changes and wood harvesting on the global carbon cycle, globally important LCLM practices with relevance also for physical climate and economic production.

We conduct idealized global experiments of deforestation, afforestation and wood harvesting over a 150-year simulation period under present climate. All forcings (solar, trace gases, aerosols) are held constant at present-day levels to isolate the climatic effects from different LCLM scenarios on the carbon cycle. All experiments are conducted by three different Earth system models (MPI-ESM, EC-EARTH and CESM) to quantify inter-model uncertainty and potentially uncover specific model biases. The analysis focuses on the transient response of carbon fluxes after the LCLM practice is in order to unravel model differences concerning temporal dynamics of LCLM effects and to show how quickly signals emerge that could potentially mitigate climate change.

With this research, we will provide a deeper understanding about simulated LCLM effects on the carbon cycle and also report model uncertainties. Together with parallel efforts to quantify biogeophysical effects of LCLM, our study will also lead to assess the overall potential of LCLM as a means for land-based climate mitigation.

How to cite: Guo, S., Pongratz, J., Havermann, F., Alessandri, A., Coumou, D., Davin, E. L., Hertog, S. D., Lejeune, Q., Manola, I., Menke, I., Schleussner, C., Seneviratne, S. I., and Thiery, W.: Biogeochemical effects of land cover and land management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19737, https://doi.org/10.5194/egusphere-egu2020-19737, 2020.

As a carbon dioxide removal measure, the Norwegian government is currently considering a policy of large-scale planting of spruce (Picea abies (L) H. Karst) on non-forested lands (i.e., aff-/reforestation) and secondary forested lands dominated by early successional broadleaved tree species (i.e., improved forest management).  Given the need to achieve net zero emissions in the latter half of the 21^st century in effort to limit the global mean temperature rise to “well below” 2 °C, the mitigation potential of such a policy is unclear given relatively slow tree growth rates in the region.  Further convoluting the picture is the magnitude and relevance of surface albedo changes linked to such projects, which typically counter the benefits of an enhanced forest CO₂ sink in high latitude regions.  Here, we carry out a rigorous empirical assessment of the terrestrial carbon dioxide removal (tCDR) potential of large-scale aff-/reforestation (AR) and improved forest management (IFM) projects in Norway, taking into account transient developments in both terrestrial carbon sinks and surface albedo over the 21^st century and beyond.  We find that surface albedo changes would likely play a negligible role in counteracting the carbon cycle benefit of tCDR, yet given slow forest growth rates in the region, meaningful tCDR benefits from AR and IFM projects would not be realized until the end of the 21^st century, with maximum benefits occurring around 2150.  We estimate Norway’s total accumulated tCDR potential at 2100 and 2150 (including surface albedo changes) to be 447 (± 240) and 852 (± 295) Mt CO₂-eq. at mean costs of US$ 29 (± 18) and US$ 26 (± 14) per ton CDR, respectively.  For perspective, the accumulated tCDR potential at 2100 represents around 8 years of Norway’s total current annual production-based (i.e., territorial) CO₂-eq. emissions.

How to cite: Bright, R., Allen, M., Anton-Fernandez, C., Dalsgaard, L., Eisner, S., Granhus, A., Søgaard, G., and Astrup, R.: Evaluating the terrestrial carbon dioxide removal (tCDR) potential of large-scale aff-/reforestation and improved forest management in Norway, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6827, https://doi.org/10.5194/egusphere-egu2020-6827, 2020.

Land surface models (LSMs) employed in global climate research continue to struggle to predict  surface albedo in high latitude forests.  Persistent sources of error in LSMs may originate from one or more of the following:  a) the underlying land cover mapping and PFT classification; b) the parameterization of forest structure; c) canopy-snow dynamics and snow physical attributes; d) canopy radiative transfer.   Among the more sophisticated, the surface albedo scheme in the Community Land Model has undergone several updates over the past decade, although it remains unclear which updates – and to what extent -- they may have contributed to improved surface albedo prediction accuracy in high latitude forest environments.  Here, using Fennoscandia (Norway, Sweden, and Finland) as a case study region and a 5-year MODIS-based surface albedo time series as an empirical benchmark, we carry out a series of offline simulations using CLM versions 4.5, 5.0, and FATES (formerly ED) combined with novel land cover and structure mapping to systematically quantify errors attributable to the aforementioned sources, as well as improvements (or degradations) to predictive performance associated with incremental model developments in time.  Preliminary results using CLM v. 4.5 & 5.0 suggest that both the underlying land cover mapping and the representation of forest structure contribute equally to prediction error and outweigh the error attributable to the parameterization of canopy-snow processes.  As for canopy radiative transfer, the extent to which the multi-layer canopy radiative transfer scheme introduced in FATES reduces surface albedo prediction error over the single-layer scheme employed in all other CLM versions remains to be quantified.

How to cite: Tang, H., Bright, R., and Stordal, F.: A systematic evaluation of surface albedo prediction error in high latitude forest environments: The case of the Community Land Model (CLM), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14730, https://doi.org/10.5194/egusphere-egu2020-14730, 2020.

Feedbacks between biosphere and other components of the Earth system are challenging to model accurately and therefore are often omitted or oversimplified in Earth system models (ESMs). However, their importance is increasingly recognized as rapid disturbances due to anthropogenic (e.g., deforestation) or natural (e.g. regional increase in fires) drivers are already observed.

Here we couple the well established and comprehensively validated dynamic global vegetation model LPJmL5.1 (von Bloh et al., 2018) to an Earth System model CM2Mc (MOM5/AM2, Galbraith et al. 2011). We replace the simple static vegetation model LaD with LPJmL5.1 and couple the water- and energy cycle by using GFDL’s Flexible Modeling System (FMS). In order to stabilize the model performance, several adjustments to LPJmL5.1 had to be done, including the introduction of a subdaily cycle for the energy and water calculations, the implementation of a conductance of the soil evaporation and plant interception, the calculation of a canopy layer humidity, and the surface energy balance in order to calculate the surface and canopy layer temperature within LPJmL5.1.

The coupled system allows us to answer questions regarding ecosystem stability with a complete energy and water cycle. For example, changes in the vegetation have a large impact on atmosphere dynamics, which in turn affects precipitation and feeds back into vegetation growth and mortality. To examine this feedback a simple experiment is performed by deforesting the whole Amazon basin and replacing it with grassland. Our results show decreasing precipitation and increasing canopy temperature which becomes a stable climate state in this treeless scenario. Future applications of the coupled model may include the investigation of tipping points in the biosphere, the impact of different atmospheric CO₂ concentrations or climate change and land-use change scenarios.

How to cite: Drüke, M., von Bloh, W., Petri, S., Schaphoff, S., Sakschewski, B., Forkel, M., Huiskamp, W., Feulner, G., and Thonicke, K.: Coupling the dynamic vegetation model LPJmL5.1 to an Earth system model – towards POEM1.0, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17982, https://doi.org/10.5194/egusphere-egu2020-17982, 2020.

The effects of vegetation and land use/land cover maps on surface energy and carbon fluxes predictions from land surface model are investigated. The model is applied at global scale and a comparison between two configurations using different land cover maps is performed. In the first configuration, the land cover is based on the operational GLCCv1.2 map, in the second the ESA-CCI land cover map is used.

Based on these two configurations, the observation operator that disaggregates the satellite-based leaf area index into high and low vegetation components is also modified to ensure optimal conservation of the observed LAI. The Seasonal variability of the vegetation cover is also investigated by introducing a modified lamber-beer formulation that allows varying the vegetation cover as a function of the LAI.

How to cite: Boussetta, S., Balsamo, G., Arduini, E., Nogueira, M., Arduini, G., Choulga, M., Wedi, N., and Munoz Sabater, J.: Sensitivity of the ECMWF Land surface model to vegetation and LU/LC maps, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22238, https://doi.org/10.5194/egusphere-egu2020-22238, 2020.

Changes of forest cover regulate climate system directly through the alteration of water vapor, energy, and momentum exchange between land surface and the atmosphere. These land-based biophysical effects vary with locations and seasons and cause regional cooling or warming, which enhances or diminishes the climatic benefits of forest carbon drawdown in different cases. Biophysical climate effects of forest conversion exhibit the largest uncertainty in the mid-latitudes. The sign and magnitude of biophysical effect in temperate zones are still under hot debate. Over the past two decades, most of our understandings on how forest affects climate through biophysical processes came from sensitivity analysis of climate modeling, by comparing paired model simulations of forest and short vegetation covers. However, much remains unknown in the real world due to the complicated process and uncertainty in magnitude, especially in the temperate bioclimate regions. Here we reviewed complex results and debates from model simulation, field measurements, and satellite observation, and then applied satellite-based observation to investigate the biophysical climate response to potential forest conversion in temperate regions, especially on the spatial and temporal patterns and underlying mechanisms. We also interpret some key findings on land-climate interactions from recent IPCC special report on climate change and land (SRCCL).

Readings:

Jia, G., E. Shevliakova, P. Artaxo, et al. (2019): Land-climate interactions, in Skea J. et al. (eds.) IPCC Special report on climate change and land. Intergovernmental Panel on Climate Change. IPCC, Geneva (in press) https://www.ipcc.ch/srccl/chapter/chapter-2/

Ma, W., G. Jia, and A. Zhang (2017): Multiple satellite-based analysis reveals complex climate effects of temperate forests and related energy budget, 
J. Geophys. Res. Atmos., 122, 3806–3820, doi:10.1002/2016JD026278

Web: green.tea.ac.cn

How to cite: Jia, G.: Biophysical climate effects of changing forest cover across scales, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4071, https://doi.org/10.5194/egusphere-egu2020-4071, 2020.