| 1. |
- Bayer, Anita D., et al.
(författare)
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Uncertainties in the land-use flux resulting from land-use change reconstructions and gross land transitions
- 2017
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Ingår i: Earth System Dynamics. - : Copernicus GmbH. - 2190-4979 .- 2190-4987. ; 8:1, s. 91-111
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Tidskriftsartikel (refereegranskat)abstract
- Land-use and land-cover (LUC) changes are a key uncertainty when attributing changes in measured atmospheric CO2 concentration to its sinks and sources and must also be much better understood to determine the possibilities for land-based climate change mitigation, especially in the light of human demand on other land-based resources. On the spatial scale typically used in terrestrial ecosystem models (0.5 or 1°) changes in LUC over time periods of a few years or more can include bidirectional changes on the sub-grid level, such as the parallel expansion and abandonment of agricultural land (e.g. in shifting cultivation) or cropland-grassland conversion (and vice versa). These complex changes between classes within a grid cell have often been neglected in previous studies, and only net changes of land between natural vegetation cover, cropland and pastures accounted for, mainly because of a lack of reliable high-resolution historical information on gross land transitions, in combination with technical limitations within the models themselves. In the present study we applied a state-of-The-Art dynamic global vegetation model with a detailed representation of croplands and carbon-nitrogen dynamics to quantify the uncertainty in terrestrial ecosystem carbon stocks and fluxes arising from the choice between net and gross representations of LUC. We used three frequently applied global, one recent global and one recent European LUC datasets, two of which resolve gross land transitions, either in Europe or in certain tropical regions. When considering only net changes, land-use-Transition uncertainties (expressed as 1 standard deviation around decadal means of four models) in global carbon emissions from LUC (ELUC) are ±0.19, ±0.66 and ±0.47gCa1 in the 1980s, 1990s and 2000s, respectively, or between 14 and 39% LUC. Carbon stocks at the end of the 20th century vary by ±11 PgC for vegetation and ±37PgC for soil C due to the choice of LUC reconstruction, i.e. around 3% of the respective C pools. Accounting for sub-grid (gross) land conversions significantly increased the effect of LUC on global and European carbon stocks and fluxes, most noticeably enhancing global cumulative ELUC by 33PgC(1750-2014) and entailing a significant reduction in carbon stored in vegetation, although the effect on soil C stocks was limited. Simulations demonstrated that assessments of historical carbon stocks and fluxes are highly uncertain due to the choice of LUC reconstruction and that the consideration of different contrasting LUC reconstructions is needed to account for this uncertainty. The analysis of gross, in addition to net, land-use changes showed that the full complexity of gross land-use changes is required in order to accurately predict the magnitude of LUC change emissions. This introduces technical challenges to process-based models and relies on extensive information regarding historical land-use transitions.
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| 2. |
- Bodin, Per, et al.
(författare)
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Optimizing cropland cover for stable food production in Sub-Saharan Africa using simulated yield and Modern Portfolio Theory
- 2014
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Ingår i: Earth System Dynamics Discussion. - : Copernicus GmbH. - 2190-4995. ; 5, s. 1571-1606
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Food security can be defined as stable access to food of good nutritional quality. In Sub Saharan Africa access to food is strongly linked to local food production and the capacity to generate enough calories to sustain the local population. Therefore it is important in these regions to generate not only sufficiently high yields but also to reduce interannual variability in food production. Traditionally, climate impact simulation studies have focused on factors that underlie maximum productivity ignoring the variability in yield. By using Modern Portfolio Theory, a method stemming from economics, we here calculate optimum current and future crop selection that maintain current yield while minimizing variance, vs. maintaining variance while maximizing yield. Based on simulated yield using the LPJ-GUESS dynamic vegetation model, the results show that current cropland distribution for many crops is close to these optimum distributions. Even so, the optimizations displayed substantial potential to either increase food production and/or to decrease its variance regionally. Our approach can also be seen as a method to create future scenarios for the sown areas of crops in regions where local food production is important for food security.
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| 3. |
- Frieler, Katja, et al.
(författare)
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Understanding the weather signal in national crop-yield variability
- 2017
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Ingår i: Earth's Future. - 2328-4277. ; 5:6, s. 605-616
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Tidskriftsartikel (refereegranskat)abstract
- Year-to-year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather-induced crop-yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state-of-the-art, process-based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop-yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process-based crop models not only account for weather influences on crop yields, but also provide options to represent human-management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.
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| 4. |
- Hantson, Stijn, et al.
(författare)
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Global isoprene and monoterpene emissions under changing climate, vegetation, CO2 and land use
- 2017
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Ingår i: Atmospheric Environment. - : Elsevier BV. - 1352-2310. ; 155, s. 35-45
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Tidskriftsartikel (refereegranskat)abstract
- Plants emit large quantities of isoprene and monoterpenes, the main components of global biogenic volatile organic compound (BVOC) emissions. BVOCs have an important impact on the atmospheric composition of methane, and of short-lived radiative forcing agents (e.g. ozone, aerosols etc.). It is therefore necessary to know how isoprene and monoterpene emissions have changed over the past and how future changes in climate, land-use and other factors will impact them. Here we present emission estimates of isoprene and monoterpenes over the period 1901–2 100 based on the dynamic global vegetation model LPJ-GUESS, including the effects of all known important drivers. We find that both isoprene and monoterpene emissions at the beginning of the 20th century were higher than at present. While anthropogenic land-use change largely drives the global decreasing trend for isoprene over the 20th century, changes in natural vegetation composition caused a decreasing trend for monoterpene emissions. Future global isoprene and monoterpene emissions depend strongly on the climate and land-use scenarios considered. Over the 21st century, global isoprene emissions are simulated to either remain stable (RCP 4.5), or decrease further (RCP 8.5), with important differences depending on the underlying land-use scenario. Future monoterpene emissions are expected to continue their present decreasing trend for all scenarios, possibly stabilizing from 2050 onwards (RCP 4.5). These results demonstrate the importance to take both natural vegetation dynamics and anthropogenic changes in land-use into account when estimating past and future BVOC emissions. They also indicate that a future global increase in BVOC emissions is improbable.
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| 5. |
- Müller, Christoph, et al.
(författare)
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Global gridded crop model evaluation : Benchmarking, skills, deficiencies and implications
- 2017
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Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 10:4, s. 1403-1422
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Tidskriftsartikel (refereegranskat)abstract
- Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.
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