ALBERT

Description: Existing dynamic global vegetation models (DGVMs) have a~limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus to enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a~new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal to decadal dynamics of vegetation greenness.

Description: Extreme meteorological events are most likely to occur more often with climate change, leading to a further acceleration of climate change through potentially devastating effects on terrestrial ecosystems. But not all extreme meteorological events lead to extreme ecosystem response. Unlike most current studies, we therefore focus on pre-defined hazardous ecosystem behaviour and the identification of coinciding meteorological conditions, instead of expected ecosystem damage for a pre-defined meteorological event. We use a simple probabilistic risk assessment based on time series of ecosystem behaviour and meteorological conditions. Given the risk assessment terminology, vulnerability and risk for the previously defined hazard are, thus, estimated on the basis of observed hazardous ecosystem behaviour. We first adapt this generic approach to extreme responses of terrestrial ecosystems to drought and high temperatures, with defining the hazard as a negative net biome productivity over a 12 months period. Further, we show an instructive application for two selected sites using data for 1981–2010; and then apply the method on pan-European scale addressing the 1981–2010 period and future projections for 2071–2100, both based on numerical modelling results (LPJmL for ecosystem behaviour; REMO-SRES A1B for climate). Our site-specific results demonstrate the applicability of the proposed method, using the SPEI index to describe the meteorological condition. They also provide examples for their interpretation in case of vulnerability to drought for Spain with the expected value of the SPEI being 0.4 lower for hazardous than for non-hazardous ecosystem behaviour, and of non-vulnerability for Northern Germany, where the expected drought index value for hazard observations relates to wetter conditions than for the non-hazard observations. The pan-European assessment shows that significant results could be obtained for large areas within Europe. For 2071–2100 they indicate a shift towards vulnerability to drought, mainly in the central and north-eastern parts of Europe, where negative net biome productivity was not used to be associated with drought. In Southern parts of Europe, considerable vulnerability and risk to drought have been identified already under current conditions; in future, the difference in SPEI between hazardous and non-hazardous ecosystem behaviour as well as the frequency of hazardous ecosystem behaviour will increase further. Vulnerability decreased only for the border region between Ukraine, Russia and Belarus, where a change in ecosystem types occurred with less vulnerable plant species in the future. These first model-based applications indicate the conceptional advantages of the proposed method by focusing on the identification of critical meteorological conditions for which we observe hazardous ecosystem behaviour in the analysed dataset. Application of the method to empirical time series would be an important next step to test the methods.

Description: We analyse how climate change may alter risks posed by droughts to carbon fluxes in European ecosystems. The approach follows a recently proposed framework for risk analysis based on probability theory. In this approach, risk is quantified as the product of hazard probability and ecosystem vulnerability. The probability of a drought hazard is calculated here from the Standardised Precipitation Evapotranspiration Index. Vulnerability is calculated from the response to drought simulated by process-based vegetation models. Here we use six different models: three for generic vegetation (JSBACH, LPJmL, ORCHIDEE) and three for specific ecosystems (Scots pine forests: BASFOR; winter wheat fields: EPIC; grasslands: PASIM). The periods 1971–2000 and 2071–2100 are compared. Climate data are based on observations and on output from the regional climate model REMO using the SRES A1B scenario. The risk analysis is carried out for ∼22 000 grid cells of 0.25° × 0.25° across Europe. For each grid cell, drought vulnerability and risk are quantified for five seasonal variables: net primary and ecosystem productivity (NPP, NEP), heterotrophic respiration (RH), soil water content and evapotranspiration. Climate change is expected to lead to increased drought risks to net primary productivity in the Mediterranean area: five of the models estimate that risk will exceed 15%. The risks will increase mainly because of greater drought probability; ecosystem vulnerability will increase to lesser extent. Because NPP will be affected more than RH, future C-sequestration (NEP) will also be at risk predominantly in southern Europe, with risks exceeding 0.25 g C m−2 d−1 according to most models, amounting to reductions in carbon sequestration of 20 to 80%.

Description: A process-based fire regime model (SPITFIRE) has been developed, coupled with ecosystem dynamics in the LPJ Dynamic Global Vegetation Model, and used to explore spatial and temporal patterns of fire regimes and the current impact of fire on the terrestrial carbon cycle and associated emissions of trace atmospheric constituents. The model estimates an average release of 2.24 Pg C yr−1 as CO2 from biomass burning during the 1980s and 1990s. Comparison with observed active fire counts shows that the model reproduces where fire occurs and can mimic broad geographic patterns in the peak fire season, although the predicted peak is 1–2 months late in some regions. Modelled fire season length is generally overestimated by about one month, but shows a realistic pattern of differences among biomes. Comparisons with remotely sensed burnt-area products indicate that the model reproduces broad geographic patterns of annual fractional burnt area over most regions, including the boreal forest, although interannual variability in the boreal zone is underestimated. Overall SPITFIRE produces realistic simulations of spatial and temporal patterns of fire under modern conditions and of the current impact of fire on the terrestrial carbon cycle and associated emissions of trace greenhouse gases and aerosols.

Description: Climate extremes can trigger exceptional responses in terrestrial ecosystems, for instance by altering growth or mortality rates. Effects of this kind are often manifested in reductions of the local net primary production (NPP). Investigating a set of European long-term data on annual radial tree growth confirms this pattern: we find that 53% of tree ring width (TRW) indices are below one standard deviation, and up to 16% of the TRW values are below two standard deviations in years with extremely high temperatures and low precipitation. Based on these findings we investigate if climate driven patterns in long-term tree growth data may serve as benchmarks for state-of-the-art dynamic vegetation models such as LPJmL. The model simulates NPP but not explicitly the radial tree ring growth, hence requiring a generic method to ensure an objective comparison. Here we propose an analysis scheme that quantifies the coincidence rate of climate extremes with some biotic responses (here TRW or simulated NPP). We find that the reduction in tree-ring width during drought extremes is lower than the corresponding reduction of simulated NPP. We identify ten extreme years during the 20th century in which both, model and measurements indicate high coincidence rates across Europe. However, we detect substantial regional differences in simulated and observed responses to extreme events. One explanation for this discrepancy could be that the tree-ring data have preferentially been sampled at more climatically stressed sites. The model-data difference is amplified by the fact that dynamic vegetation models are designed to simulate mean ecosystem responses at landscape or regional scale. However, we find that both model-data and measurements display carry-over effects from the previous year. We conclude that using radial tree growth is a good basis for generic model-benchmarks if the data are analyzed by scale-free measures such as coincidence analysis. Our study shows strong reductions in carbon sequestration during extreme years. However, for a better understanding of the impact of extreme events on e.g. the long-term fate of the European carbon balance, more long-term measurement data and improved process-based models are needed.

Description: Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer-term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the Arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and Arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal-to-decadal dynamics of vegetation greenness.

Description: A process-based fire regime model (SPITFIRE) has been developed, coupled with ecosystem dynamics in the LPJ Dynamic Global Vegetation Model, and used to explore fire regimes and the current impact of fire on the terrestrial carbon cycle and associated emissions of trace atmospheric constituents. The model estimates an average release of 2.24 Pg C yr−1 as CO2 from biomass burning during the 1980s and 1990s. Comparison with observed active fire counts shows that the model reproduces where fire occurs and can mimic broad geographic patterns in the peak fire season, although the predicted peak is 1–2 months late in some regions. Modelled fire season length is generally overestimated by about one month, but shows a realistic pattern of differences among biomes. Comparisons with remotely sensed burnt-area products indicate that the model reproduces broad geographic patterns of annual fractional burnt area over most regions, including the boreal forest, although interannual variability in the boreal zone is underestimated.

Description: Exchange of non-CO2 trace gases between the land surface and the atmosphere plays an important role in atmospheric chemistry and climate. Recent studies have highlighted its importance for interpretation of glacial-interglacial ice-core records, the simulation of the pre-industrial and present atmosphere, and the potential for large climate-chemistry and climate-aerosol feedbacks in the coming century. However, spatial and temporal variations in trace gas emissions and the magnitude of future feedbacks are a major source of uncertainty in atmospheric chemistry, air quality and climate science. To reduce such uncertainties Dynamic Global Vegetation Models (DGVMs) are currently being expanded to mechanistically represent processes relevant to non-CO2 trace gas exchange between land biota and the atmosphere. In this paper we present a review of important non-CO2 trace gas emissions, the state-of-the-art in DGVM modelling of processes regulating these emissions, identify key uncertainties for global scale model applications, and discuss a methodology for model integration and evaluation.

Description: We analyse how climate change may alter risks posed by droughts to carbon fluxes in European ecosystems. The approach follows a recently proposed framework for risk analysis based on probability theory. In this approach, risk is quantified as the product of hazard probability and ecosystem vulnerability. The probability of a drought hazard is calculated here from the Standardized Precipitation–Evapotranspiration Index (SPEI). Vulnerability is calculated from the response to drought simulated by process-based vegetation models. We use six different models: three for generic vegetation (JSBACH, LPJmL, ORCHIDEE) and three for specific ecosystems (Scots pine forests: BASFOR; winter wheat fields: EPIC; grasslands: PASIM). The periods 1971–2000 and 2071–2100 are compared. Climate data are based on gridded observations and on output from the regional climate model REMO using the SRES A1B scenario. The risk analysis is carried out for ~ 18 000 grid cells of 0.25 × 0.25° across Europe. For each grid cell, drought vulnerability and risk are quantified for five seasonal variables: net primary and ecosystem productivity (NPP, NEP), heterotrophic respiration (Rh), soil water content and evapotranspiration. In this analysis, climate change leads to increased drought risks for net primary productivity in the Mediterranean area: five of the models estimate that risk will exceed 15%. The risks increase mainly because of greater drought probability; ecosystem vulnerability will increase to a lesser extent. Because NPP will be affected more than Rh, future carbon sequestration (NEP) will also be at risk predominantly in southern Europe, with risks exceeding 0.25 g C m−2 d−1 according to most models, amounting to reductions in carbon sequestration of 20 to 80%.