地球科学最新文献

In this contribution, we introduce some new theory for the classical GNSS ambiguity function (AF) method. We provide the probability model by means of which the AF-estimator becomes a maximum likelihood estimator, and we provide a globally convergent algorithm for computing the AF-estimate. The algorithm is constructed from combining the branch-and-bound principle, with a special convex relaxation of the multimodal ambiguity function, to which the projected-gradient-descent method is applied to obtain the required bounds. We also provide a systematic comparison between the AF-principle and that of integer least-squares (ILS). From this comparison, the conclusion is reached that the two principles are fundamentally different, although there are identified circumstances under which one can expect AF- and ILS-solutions to behave similarly.

Between 1985 and 2014 observations show an expansion of Southern Ocean sea-ice. This phenomena is not simulated in CMIP6 Atmosphere-Ocean General Circulation Models (AOGCMs). Here we quantify the impact of this discrepancy on radiative feedback and simulated global temperature trends. We find that both satellite reconstructions of the Earth's energy budget and atmosphere-only GCM simulations forced with observed Sea Surface Temperature (SST) and sea-ice trends support the hypothesis of a negative surface albedo feedback over the Southern Ocean. In contrast, declining sea-ice in AOGCMs gives rise to a positive feedback. We estimate that had the AOGCMs simulated the observed Southern Ocean sea-ice evolution their global feedback parameter would be less positive by 0.07–0.23 $��\text{W}��{\text{m}}^{�-�2�}��K^{�-�1�}�$ (which is 12%–29% of the total difference between AOGCM simulations and simulations forced with observed SST and sea-ice) and their global temperature trend over this 30 year period reduced by 0.01–0.06 K $��{\text{decade}}^{�-�1�}�$.

The use of timber harvesting and skidding machines significantly alters a catchment's hydrological processes due to soil compaction effects. Although it is obvious that the use of heavy forest machines increases surface runoff and water yield, quantifying these effects remains challenging. This research aimed at exploring how physically based hydrological models are suited for investigating the effects of soil compaction on hydrologic responses at the catchment scale. We employed a process-based SWAT+ hydrological model to assess the impact of temporary (e.g., skid trails) and more permanent timber harvest infrastructure on the hydrologic response of the Palatinate Forest Biosphere Reserve in Germany. We specifically analysed the role of soil compaction in hydrological processes by simulating water fluxes under three scenarios: uncompacted soil (baseline), compacted soil during harvest and skidding operations (Scenario-1) and due to permanently constructed infrastructures (Scenario-2). Results demonstrated substantial alterations in water fluxes under Scenarios-1 and -2 compared to the baseline, with annual average surface runoff increasing by 74% and 125%, respectively, and lateral flow decreasing by 14% and 26%. These changes varied notably between steep and low slope areas. Steep slope regions exhibited significantly larger increases in surface runoff, while low slope areas experienced greater reductions in lateral flow and percolation. This differentiation underlines the combined influence of management scenarios and slope, highlighting the critical need for forest harvesting strategies that account for spatial variability and the types of machinery employed.

Global terrestrial water storage anomaly (TWSA) products from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE/FO) have an approximately three-month latency, significantly limiting their operational use in water management and drought monitoring. To address this challenge, we develop a Bayesian convolutional neural network (BCNN) to predict TWSA fields with uncertainty estimates during the latency period. The results demonstrate that BCNN provides near-real-time TWSA estimates that closely match GRACE/FO observations, with median correlation coefficients of 0.92–0.95, Nash-Sutcliffe efficiencies of 0.81–0.89, and root mean squared errors of 1.79–2.26 cm for one- to three-month ahead predictions. More importantly, the model advances global hydrological drought monitoring by enabling detection up to three months before GRACE/FO data availability, with median characterization mismatches below 16.4%. This breakthrough in early warning capability addresses a fundamental constraint in satellite-based hydrological monitoring and offers water resource managers critical lead time to implement drought mitigation strategies.