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Groundwater is one of the most essential natural resources and is affected by climate variability. However, our understanding of the effects of climate on groundwater recharge (R), particularly in dry regions, is limited. Future climate projections suggest changes in many statistical characteristics of the potential evapotranspiration (Ep) and the rainfall that dictate the R. To better understand the relationship between climate statistics and R, we separately considered changes to the mean, standard deviation, and extreme statistics of the Ep and the precipitation (P). We simulated the R under different climate conditions in multiple semi-arid and arid locations worldwide. Obviously, lower precipitation is expected to result in lower groundwater recharge and vice versa. However, the relationship between R and P is non-linear. Examining the ratio R/P is useful for revealing the underlying relation between R and P; therefore, we focus on this ratio. We find that changes in the average Ep have the most significant impact on R/P. Interestingly, we find that changes in the extreme Ep statistics have much weaker effects on R/P than changes in extreme P statistics. Contradictory results of previous studies and predictions of future groundwater recharge may be explained by the differences in the projected climate statistics.

Soil tillage, crop residue management, nutrient management, and pest management are among the core farming practices. Each of these practices impacts a range of soil functions and ecosystem services, including water availability for crops, weed control, insect and pathogen control, soil quality and functioning, soil erosion control, soil organic carbon pool, environmental pollution control, greenhouse gas refuse, and crop yield productivity. In this study, we reviewed relevant bibliography and then developed a simple conceptual model, in which these soil functions and ecosystem services were scored and compared between conventional, conservation, and integrated agricultural systems. Using this conceptual model revealed that the overall agro-environmental score, excluding crop yield productivity, is largest for conservation systems (71.9 %), intermediate for integrated systems (68.8 %), and the smallest for conventional systems (52.1 %). At the same time, the crop yield productivity score is largest for integrated systems (83.3 %), intermediate for conventional systems (66.7 %), and the smallest for conservation systems (58.3 %). This study shows the potential of moderate-intensity and integrated farming systems in carrying on global food security while adequately sustaining environmental quality and ecosystem services.

A better understanding the effects of rainfall and evapotranspiration statistics on groundwater recharge (GR) requires long time series of these variables. However, long records of the relevant variables are scarce. To overcome this limitation, time series of rainfall and evapotranspiration are often synthesized using different methods. Here, we attempt to study the dependence of estimated GR on the synthesis methods used. We focus on regions with semi-arid climate conditions and soil types. For this purpose, we used longer than 40 year records of the daily rain and climate variables that are required to calculate the potential evapotranspiration (ETref), which were measured in two semi-arid locations.These locations, Beit Dagan and Shenmu, have aridity indices of 0.39 and 0.41, respectively, and similar seasonal and annual ETref rates (1370 and 1030 mm yr−1, respectively) but different seasonal rain distributions. Stochastic daily rain and ETref time series were synthesized according to the monthly empirical distributions. This synthesis method does not preserve the monthly and annual rain and ETref distributions. Therefore, we propose different correction methods to match the synthesized and measured time series' annual or monthly statistics. GR fluxes were calculated using the 1D Richards equation for four typical semi-arid soil types, and by prescribing the synthesized rain and ETref as atmospheric conditions. The estimated GR fluxes are sensitive to the synthesis method. However, the ratio between the GR and the total rain does not show the same sensitivity. The effects of the synthesis methods are shown to be the same for both locations, and correction of the monthly mean and SD of the synthesized time series results in the best agreement with independent estimates of the GR. These findings suggest that the assessment of GR under current and future climate conditions depends on the synthesis method used for rain and ETref.

Climate predictions are only meaningful if the associated uncertainty is reliably estimated. A standard practice is to use an ensemble of climate model projections. The main drawbacks of this approach are the fact that there is no guarantee that the ensemble projections adequately sample the possible future climate conditions. Here, we suggest using simulations and measurements of past conditions in order to study both the performance of the ensemble members and the relation between the ensemble spread and the uncertainties associated with their predictions. Using an ensemble of CMIP5 long-term climate projections that was weighted according to a sequential learning algorithm and whose spread was linked to the range of past measurements, we find considerably reduced uncertainty ranges for the projected global mean surface temperature. The results suggest that by employing advanced ensemble methods and using past information, it is possible to provide more reliable and accurate climate projections. The ensemble spread of climate models is often interpreted as the uncertainty of the projection, but this is not always justified. Applying learning algorithms to an ensemble of climate predictions allows for a significant uncertainty reduction of projected global mean surface temperatures compared to the ensemble spread.

Large responses of ecosystems to small changes in the conditions—regime shifts—are of great interest and importance. In spatially extended ecosystems, these shifts may be local or global. Using empirical data and mathematical modeling, we investigated the dynamics of the Namibian fairy circle ecosystem as a case study of regime shifts in a pattern-forming ecosystem. Our results provide new support, based on the dynamics of the ecosystem, for the view of fairy circles as a self-organization phenomenon driven by water–vegetation interactions. The study further suggests that fairy circle birth and death processes correspond to spatially confined transitions between alternative stable states. Cascades of such transitions, possible in various pattern-forming systems, result in gradual rather than abrupt regime shifts.

Ensembles of climate models are commonly used to improve decadal climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, an ensemble of decadal climate predictions is used to demonstrate the ability of sequential learning algorithms (SLAs) to reduce the forecast errors and reduce the uncertainties. Three different SLAs are considered, and their performances are compared with those of an equally weighted ensemble, a linear regression, and the climatology. Predictions of four different variables—the surface temperature, the zonal and meridional wind, and pressure—are considered. The spatial distributions of the performances are presented, and the statistical significance of the improvements achieved by the SLAs is tested. The reliability of the SLAs is also tested, and the advantages and limitations of the different measures of the performance are discussed. It was found that the best performances of the SLAs are achieved when the learning period is comparable to the prediction period. The spatial distribution of the SLAs performance showed that they are skillful and better than the other forecasting methods over large continuous regions. This finding suggests that, despite the fact that each of the ensemble models is not skillful, they were able to capture some physical processes that resulted in deviations from the climatology and that the SLAs enabled the extraction of this additional information.

Critical transitions are of great interest to scientists in many fields. Most knowledge about these transitions comes from systems exhibiting the multistability of spatially uniform states. In spatially extended and, particularly, in pattern-forming systems, there are many possible scenarios for transitions between alternative states. Quenched disorder may affect the dynamics, bifurcation diagrams and critical transitions in nonlinear systems. However, only a few studies have explored the effects of quenched disorder on pattern-forming systems, either experimentally or by using theoretical models. Here, we use a fundamental model describing pattern formation, the Swift-Hohenberg model and a well-explored mathematical model describing the dynamics of vegetation in drylands to study the effects of quenched disorder on critical transitions in pattern-forming systems. We find that the disorder affects the patterns formed by introducing an interplay between the imposed pattern and the self-organized one. We show that, in both systems considered here, the disorder significantly increases the durability of the patterned state and makes the transition between the patterned state and the uniform state more gradual. In addition, the disorder induces hysteresis in the response of the system to changes in the bifurcation parameter well before the critical transition occurs. We also show that the cross-correlation between the disordered parameter and the dynamical variable can serve as an early indicator for an imminent critical transition.

Drylands are pattern-forming systems showing self-organized vegetation patchiness, multiplicity of stable states and fronts separating domains of alternative stable states. Pattern dynamics, induced by droughts or disturbances, can result in desertification shifts from patterned vegetation to bare soil. Pattern formation theory suggests various scenarios for such dynamics: an abrupt global shift involving a fast collapse to bare soil, a gradual global shift involving the expansion and coalescence of bare-soil domains and an incipient shift to a hybrid state consisting of stationary bare-soil domains in an otherwise periodic pattern. Using models of dryland vegetation, we address the question of which of these scenarios can be realized. We found that the models can be split into two groups: models that exhibit multiplicity of periodic-pattern and bare-soil states, and models that exhibit, in addition, multiplicity of hybrid states. Furthermore, in all models, we could not identify parameter regimes in which bare-soil domains expand into vegetated domains. The significance of these findings is that, while models belonging to the first group can only exhibit abrupt shifts, models belonging to the second group can also exhibit gradual and incipient shifts. A discussion of open problems concludes the paper.

Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties—internal and model variabilities. Using an ensemble of climate model simulations from the CMIP5 decadal experiments, we quantified the total uncertainty associated with these predictions and the relative importance of each source. Annual and monthly averages of the surface temperature and zonal wind were considered. We show that different definitions of the anomaly result in different conclusions regarding the variance of the ensemble members. However, some features of the uncertainty are common to all the measures we considered. We found that on decadal time scales, there is no considerable increase in the uncertainty with time. The model variability is more sensitive to the annual cycle than the internal variability. This, in turn, results in a maximal uncertainty during the winter in the northern hemisphere. The uncertainty of the surface temperature prediction is dominated by the model variability, whereas the uncertainty of the zonal wind is determined by both sources. An analysis of the spatial distribution of the uncertainty reveals that the surface temperature has higher variability over land and in high latitudes, whereas the surface zonal wind has higher variability over the ocean. The relative importance of the internal and model variabilities depends on the averaging period, the definition of the anomaly, and the location. The model uncertainties that contribute greatly to the total uncertainties in most regions, for all the variables considered here, may be reduced by weighting the models in the ensemble.

Regional climate models (RCMs) are expected to provide better representations of the climate dynamics because of their higher spatial resolutions. Here, we generated an ensemble of decadal (2006–36) RCM predictions for the area of Israel, which spans a considerable climatic gradient and comprises complex terrain. We used the WRF Model forced by the MIROC5 global climate model (GCM). The ensemble was generated by choosing different combinations of radiation, microphysics, surface layer, and planetary boundary layer parameterizations. The simulation results were compared with meteorological station data for the first simulated decade. For the minimum surface temperature, all the RCM configurations performed better than the driving GCM, while for the maximum surface temperature, only three out of eight configurations improved the predictions. The RCM configurations had higher errors in predicting the precipitation, but four configurations had comparable errors to the GCM. For the next two decades, the ensemble average predicts an increase of 0.51° and 0.40°C decade−1 for the average daily minimum and maximum surface temperatures, respectively. No significant change is predicted in the precipitation. We found that all the parameterizations affect the predictions of the surface temperatures and precipitation [e.g., the CAM radiation scheme simulates colder temperatures than the RRTM for GCMs (RRTMG)] but the PBL and surface layer scheme has the largest effect on the errors. Spectral nudging was found to have a considerable effect on the deviations of the precipitation predicted by the WRF configurations from the predictions of the GCM and a much smaller effect on the surface temperature predictions.