Search Results
Benchmarking carbon fluxes of the ISIMIP2a biome models
The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E LUC) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F Jena and F CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models (p < 0.05), which is consistent with the R + L estimate (p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F Jena and F CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.
The role of storage dynamics in annual wheat prices
Identifying the drivers of global crop price fluctuations is essential for estimating the risks of unexpected weather-induced production shortfalls and for designing optimal response measures. Here we show that with a consistent representation of storage dynamics, a simple supply–demand model can explain most of the observed variations in wheat prices over the last 40 yr solely based on time series of annual production and long term demand trends. Even the most recent price peaks in 2007/08 and 2010/11 can be explained by additionally accounting for documented changes in countries' trade policies and storage strategies, without the need for external drivers such as oil prices or speculation across different commodity or stock markets. This underlines the critical sensitivity of global prices to fluctuations in production. The consistent inclusion of storage into a dynamic supply-demand model closes an important gap when it comes to exploring potential responses to future crop yield variability under climate and land-use change.
Linking sea level rise and socioeconomic indicators under the Shared Socioeconomic Pathways
In order to assess future sea level rise and its societal impacts, we need to study climate change pathways combined with different scenarios of socioeconomic development. Here, we present sea level rise (SLR) projections for the Shared Socioeconomic Pathway (SSP) storylines and different year-2100 radiative forcing targets (FTs). Future SLR is estimated with a comprehensive SLR emulator that accounts for Antarctic rapid discharge from hydrofracturing and ice cliff instability. Across all baseline scenario realizations (no dedicated climate mitigation), we find 2100 median SLR relative to 1986–2005 of 89 cm (likely range: 57–130 cm) for SSP1, 105 cm (73–150 cm) for SSP2, 105 cm (75–147 cm) for SSP3, 93 cm (63–133 cm) for SSP4, and 132 cm (95–189 cm) for SSP5. The 2100 sea level responses for combined SSP-FT scenarios are dominated by the mitigation targets and yield median estimates of 52 cm (34–75 cm) for FT 2.6 Wm−2, 62 cm (40–96 cm) for FT 3.4 Wm−2, 75 cm (47–113 cm) for FT 4.5 Wm−2, and 91 cm (61–132 cm) for FT 6.0 Wm−2. Average 2081–2100 annual SLR rates are 5 mm yr−1 and 19 mm yr−1 for FT 2.6 Wm−2 and the baseline scenarios, respectively. Our model setup allows linking scenario-specific emission and socioeconomic indicators to projected SLR. We find that 2100 median SSP SLR projections could be limited to around 50 cm if 2050 cumulative CO2 emissions since pre-industrial stay below 850 GtC, with a global coal phase-out nearly completed by that time. For SSP mitigation scenarios, a 2050 carbon price of 100 US$2005 tCO2 −1 would correspond to a median 2100 SLR of around 65 cm. Our results confirm that rapid and early emission reductions are essential for limiting 2100 SLR.
Half a degree additional warming, prognosis and projected impacts (HAPPI): Background and experimental design
The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5°C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 and 2.0°C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006–2015), the second two being estimates of a similar decade but under 1.5 and 2°C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5°C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2°C scenario.
Contrasting and interacting changes in simulated spring and summer carbon cycle extremes in European ecosystems
Climate extremes have the potential to cause extreme responses of terrestrial ecosystem functioning. However, it is neither straightforward to quantify and predict extreme ecosystem responses, nor to attribute these responses to specific climate drivers. Here, we construct a factorial experiment based on a large ensemble of process-oriented ecosystem model simulations driven by a regional climate model (12 500 model years in 1985–2010) in six European regions. Our aims are to (1) attribute changes in the intensity and frequency of simulated ecosystem productivity extremes (EPEs) to recent changes in climate extremes, CO2 concentration, and land use, and to (2) assess the effect of timing and seasonal interaction on the intensity of EPEs. Evaluating the ensemble simulations reveals that (1) recent trends in EPEs are seasonally contrasting: spring EPEs show consistent trends towards increased carbon uptake, while trends in summer EPEs are predominantly negative in net ecosystem productivity (i.e. higher net carbon release under drought and heat in summer) and close-to-neutral in gross productivity. While changes in climate and its extremes (mainly warming) and changes in CO2 increase spring productivity, changes in climate extremes decrease summer productivity neutralizing positive effects of CO2. Furthermore, we find that (2) drought or heat wave induced carbon losses in summer (i.e. negative EPEs) can be partly compensated by a higher uptake in the preceding spring in temperate regions. Conversely, however, carry-over effects from spring to summer that arise from depleted soil moisture exacerbate the carbon losses caused by climate extremes in summer, and are thus undoing spring compensatory effects. While the spring-compensation effect is increasing over time, the carry-over effect shows no trend between 1985–2010. The ensemble ecosystem model simulations provide a process-based interpretation and generalization for spring-summer interacting carbon cycle effects caused by climate extremes (i.e. compensatory and carry-over effects). In summary, the ensemble ecosystem modelling approach presented in this paper offers a novel route to scrutinize ecosystem responses to changing climate extremes in a probabilistic framework, and to pinpoint the underlying eco-physiological mechanisms.