ALBERT

Beschreibung: Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation; CV) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Beschreibung: Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52% of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2–temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dualfactor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Beschreibung: Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Beschreibung: This paper presents a study on the predefined-time (PdT) and practical PdT synchronization of competitive neural networks (CNN) in the presence of different time scales and external disturbances. Two types of external disturbances, which satisfy Lipschitz or bounded conditions, are investigated respectively. The new PdT and practical PdT stability theorems are derived in singularly perturbed systems, where the final residual set is given in detail. By employing the newly derived stability theorems, novel autonomous controllers are designed without relying on a continuous linear term and time scale parameters, while enabling PdT or practical PdT synchronization for drive-response CNNs. Additionally, upper bounds for the settling time are estimated, allowing for adjusting the predefined synchronization times regardless of the initial conditions. Finally, numerical simulations are conducted to demonstrate the effectiveness of the main results.

Beschreibung: The following authors were omitted from the original version of this Data Descriptor: Markus Reichstein and Nicolas Vuichard. Both contributed to the code development and N. Vuichard contributed to the processing of the ERA-Interim data downscaling. Furthermore, the contribution of the co-author Frank Tiedemann was re-evaluated relative to the colleague Corinna Rebmann, both working at the same sites, and based on this re-evaluation a substitution in the co-author list is implemented (with Rebmann replacing Tiedemann). Finally, two affiliations were listed incorrectly and are corrected here (entries 190 and 193). The author list and affiliations have been amended to address these omissions in both the HTML and PDF versions. © 2021, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

Beschreibung: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Beschreibung: Spherical harmonic analysis was performed on the geomagnetic Solar-quiet (Sq) variations observed at approximately 120 ground-based magnetometer stations in the mid- to low-latitude regions. The global equivalent ionospheric current system was estimated at each UT hour during 1-31 May 2020. During this period, geomagnetic activity was particularly low, and the hourly geomagnetic activity index Hpo did not exceed 4. The global Sq current intensity and pattern exhibit hour-to-hour, day-to-day, and longitudinal variability. The spatio-temporal variability of the Sq current system is complex. Fourier-wavelet transform is performed on the Sq current intensity at 31˚N and 33˚S magnetic latitudes to examine the tidal wave composition. We also evaluate the temporal variability of eastward- and westward-propagating wave components with different zonal wavenumbers (〈ems〈/em=1, 2, 3). The spectral analysis results show that the migrating (or Sun-synchronous) diurnal tidal wave component dominates considerably in the Northern and Southern Hemispheres but their day-to-day variations are not correlated. This observation indicates that the day-to-day variability of the migrating diurnal tide in Sq currents is dominated by local wind-dynamo effects, rather than by a global process such as the solar radiation effect on the ionospheric conductivity.

Beschreibung: El Niños are known to decay more rapidly, while La Niñas tend to decay more slowly. Observational analyses and coupled model experiments are conducted to show that sea surface temperature (SST) anomalies over the subtropical northeastern Pacific (SNEP) and equatorial western Pacific (EWP) are key factors to determine the decay pace of the El Niño and La Niña and their asymmetry. In the present climate the La Niña produce larger cold SST anomalies over the regions than the warm SST anomalies produced by the El Niño. The magnitude difference over the SNEP and EWP help to slow down the La Niña decay via subtropical footprinting and tropical thermocline variation mechanisms, respectively. CMIP6 models project the magnitude differences of SNEP SST anomalies between El Niño and La Niña to reduce in the future warming world, causing the asymmetric El Niño-La Niña decay to weaken.

Beschreibung: Several efforts have been made recently to develop new tools based on ionospheric indices derived from the Global Navigation Satellite System (GNSS) data to improve our knowledge of the effects of Magnetic Storm and Equatorial Plasma Bubbles (EPB) over the ionosphere at equatorial and low latitude regions in the American sector. Concurrently, the Ksa (K for South America), a local geomagnetic index have been developed to overcome the need for observatories in the South American sector providing data for binding the “planetary” Kp index, widely used as a global response to space weather disturbances. In the present work, we present and discuss results from studies recently published and under preparation related to the use of these indices. Concerning the ionospheric indices, we have primarily focused on studies about the detection (or detectability), and measurement of parameters related to EPBs (latitudinal extension and velocity). Finally, we compare differences in the time evolution of the regional versus the global magnetic indices.