| 1. |
- Simpson, David, 1961, et al.
(författare)
-
Ozone - the persistent menace; interactions with the N cycle and climate change
- 2014
-
Ingår i: Current Opinion in Environmental Sustainability. - : Elsevier BV. - 1877-3435. ; 9-10, s. 9-19
-
Tidskriftsartikel (refereegranskat)abstract
- Tropospheric ozone is involved in a complex web of interactions with other atmospheric gases and particles, and through ecosystem interactions with the N-cycle and climate change. Ozone itself is a greenhouse gas, causing warming, and reductions in biomass and carbon sequestration caused by ozone provide a further indirect warming effect. Ozone also has cooling effects, however, for example, through impacts on aerosols and diffuse radiation. Ecosystems are both a source of ozone precursors (especially of hydrocarbons, but also nitrogen oxides), and a sink through deposition processes. The interactions with vegetation, atmospheric chemistry and aerosols are complex, and only partially understood. Levels and patterns of global exposure to ozone may change dramatically over the next 50 years, impacting global warming, air quality, global food production and ecosystem function.
|
|
| 2. |
- Alexander, Peter, et al.
(författare)
-
Assessing uncertainties in land cover projections
- 2017
-
Ingår i: Global Change Biology. - : Wiley. - 1354-1013. ; 23:2, s. 767-781
-
Tidskriftsartikel (refereegranskat)abstract
- Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover.
|
|
| 3. |
- Frieler, Katja, et al.
(författare)
-
Understanding the weather signal in national crop-yield variability
- 2017
-
Ingår i: Earth's Future. - 2328-4277. ; 5:6, s. 605-616
-
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.
|
|
| 4. |
- Krause, Andreas, et al.
(författare)
-
Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators
- 2017
-
Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4170 .- 1726-4189. ; 14:21, s. 4829-4850
-
Tidskriftsartikel (refereegranskat)abstract
- Land management for carbon storage is discussed as being indispensable for climate change mitigation because of its large potential to remove carbon dioxide from the atmosphere, and to avoid further emissions from deforestation. However, the acceptance and feasibility of land-based mitigation projects depends on potential side effects on other important ecosystem functions and their services. Here, we use projections of future land use and land cover for different land-based mitigation options from two land-use models (IMAGE and MAgPIE) and evaluate their effects with a global dynamic vegetation model (LPJ-GUESS). In the landuse models, carbon removal was achieved either via growth of bioenergy crops combined with carbon capture and storage, via avoided deforestation and afforestation, or via a combination of both. We compare these scenarios to a reference scenario without land-based mitigation and analyse the LPJ-GUESS simulations with the aim of assessing synergies and trade-offs across a range of ecosystem service indicators: Carbon storage, surface albedo, evapotranspiration, water runoff, crop production, nitrogen loss, and emissions of biogenic volatile organic compounds. In our mitigation simulations cumulative carbon storage by year 2099 ranged between 55 and 89 GtC. Other ecosystem service indicators were influenced heterogeneously both positively and negatively, with large variability across regions and land-use scenarios. Avoided deforestation and afforestation led to an increase in evapotranspiration and enhanced emissions of biogenic volatile organic compounds, and to a decrease in albedo, runoff, and nitrogen loss. Crop production could also decrease in the afforestation scenarios as a result of reduced crop area, especially for MAgPIE land-use patterns, if assumed increases in crop yields cannot be realized. Bioenergy-based climate change mitigation was projected to affect less area globally than in the forest expansion scenarios, and resulted in less pronounced changes in most ecosystem service indicators than forest-based mitigation, but included a possible decrease in nitrogen loss, crop production, and biogenic volatile organic compounds emissions.
|
|
| 5. |
- Olin, Stefan, et al.
(författare)
-
Modelling the response of yields and tissue C:N to changes in atmospheric CO2 and N management in the main wheat regions of western Europe
- 2015
-
Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4189. ; 12, s. 2489-2515
-
Tidskriftsartikel (refereegranskat)abstract
- Nitrogen (N) is a key element in terrestrial ecosystems as it influences both plant growth and plant interactions with the atmosphere. Accounting for carbon–nitrogen interactions has been found to alter future projections of the terrestrial carbon (C) cycle substantially. Dynamic vegetation models (DVMs) aim to accurately represent both natural vegetation and managed land, not only from a carbon cycle perspective but increasingly so also for a wider range of processes including crop yields. We present here the extended version of the DVM LPJ-GUESS that accounts for N limitation in crops to account for the effects of N fertilisation on yields and biogeochemical cycling. The performance of this new implementation is evaluated against observations from N fertiliser trials and CO2 enrichment experiments. LPJ-GUESS captures the observed response to both N and CO2 fertilisation on wheat biomass production, tissue C to N ratios (C : N) and phenology. To test the model's applicability for larger regions, simulations are subsequently performed that cover the wheat-dominated regions of western Europe. When compared to regional yield statistics, the inclusion of C–N dynamics in the model substantially increase the model performance compared to an earlier version of the model that does not account for these interactions. For these simulations, we also demonstrate an implementation of N fertilisation timing for areas where this information is not available. This feature is crucial when accounting for processes in managed ecosystems in large-scale models. Our results highlight the importance of accounting for C–N interactions when modelling agricultural ecosystems, and it is an important step towards accounting for the combined impacts of changes in climate, [CO2] and land use on terrestrial biogeochemical cycles.
|
|
| 6. |
- Prestele, Reinhard, et al.
(författare)
-
Hotspots of uncertainty in land-use and land-cover change projections : a global-scale model comparison
- 2016
-
Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 22:12, s. 3967-3983
-
Tidskriftsartikel (refereegranskat)abstract
- Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.
|
|
| 7. |
- Tchebakova, N. M., et al.
(författare)
-
Energy and Mass Exchange and the Productivity of Main Siberian Ecosystems (from Eddy Covariance Measurements). 2. Carbon Exchange and Productivity
- 2015
-
Ingår i: Biology Bulletin of the Russian Academy of Science. - 1062-3590. ; 42:6, s. 579-588
-
Tidskriftsartikel (refereegranskat)abstract
- Direct measurements of CO2 fluxes by the eddy covariance method have demonstrated that the examined middle-taiga pine forest, raised bog, true steppe, and southern tundra along the Yenisei meridian (similar to 90 degrees E) are carbon sinks of different capacities according to annual output. The tundra acts as a carbon sink starting from June; forest and bog, from May; and steppe, from the end of April. In transitional seasons and winter, the ecosystems are a weak source of carbon; this commences from September in the tundra, from October in the forest and bog, and from November in the steppe. The photosynthetic productivity of forest and steppe ecosystems, amounting to 480-530 g C/(m(2) year), exceeds by 2-2.5 times that of bogs and tundras, 200-220 g C/(m(2) year). The relationships between the heat balance structure and CO2 exchange are shown. Possible feedback of carbon exchange between the ecosystems and atmosphere as a result of climate warming in the region are assessed.
|
|
| 8. |
- Hantson, Stijn, et al.
(författare)
-
The status and challenge of global fire modelling
- 2016
-
Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4170 .- 1726-4189. ; 13:11, s. 3359-3375
-
Tidskriftsartikel (refereegranskat)abstract
- Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.
|
|
| 9. |
- Ruane, Alex C., et al.
(författare)
-
Strong regional influence of climatic forcing datasets on global crop model ensembles
- 2021
-
Ingår i: Agricultural and Forest Meteorology. - : Elsevier BV. - 0168-1923. ; 300
-
Tidskriftsartikel (refereegranskat)abstract
- We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region.
|
|
| 10. |
- Krause, Andreas, et al.
(författare)
-
Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts
- 2018
-
Ingår i: Global Change Biology. - : Wiley. - 1354-1013. ; 24:7, s. 3025-3038
-
Tidskriftsartikel (refereegranskat)abstract
- Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land-based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land-based mitigation scenarios from two land-use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ-GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land-use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land-use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land-use change. Differences between land-use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land-based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy.
|
|
