Explore the vast range of titles available.

Geothermal energy exploitation has emerged as a critical solution to combat global climate crises, such as reducing CO2 emissions and climate warming. Scaling is the process of mineral precipitation in fluid pathways and geothermal equipment. It is known to significantly hamper geothermal energy production by decreasing the rates of heat extraction. Numerous research efforts are dedicated to characterising dissolution and precipitation processes, not only to provide know-how for further and safer developments in geothermal energy, but also to adapt such findings to the ever emerging field of geothermal energy recovery. This paper presents an overview of experiments—performed under variable pressure and temperature conditions—with a focus on scaling. We assess the different factors that influence disequilibrium reactions in carbonate rocks, the different experimental setups, and their application to the field. The influence of experimental variables (such as temperature and pressure) on mineral dissolution and precipitation is discussed, and the main learning points from experiments are compared and contrasted. We address techniques for preventing and controlling scaling in geothermal wells based on a comprehensive analysis of experimental studies in carbonate rocks. We propose that the intelligent combination of fieldwork, numerical approaches, and laboratory experience provides a foundation for the success of future work in this field.

Abiological granular sludge (ABGS) formation is a potential and facile strategy for improving sludge settling performance during zinc removal from wastewater using chemical precipitation. In this study, the effect of pH, seed dosage, and flocculant dosage on ABGS formation and treated water quality was investigated. Results show that settling velocity of ABGS can reach up to 4.00 cm/s under optimal conditions, e.g., pH of 9.0, zinc oxide (ZnO) seeds dosage of 1.5 g/l, and polyacrylamide (PAM) dosage of 10 mg/l. More importantly, ABGS formation mechanism was investigated in NaOH precipitation process and compared with that in bio-polymer ferric sulfate (BPFS)–NaOH precipitation process regarding their sludge structure and composition. In the NaOH precipitation process, ABGS formation depends on some attractions between particles, such as van der Waals attraction and bridging attraction. However, during the BPFS–NaOH sludge formation process, steric repulsion becomes dominant due to the adsorption of BPFS on ZnO seeds. This repulsion further causes extremely loose structure and poor settling performance of BPFS–NaOH sludge.

As an essential moisture transport channel over the Tibetan Plateau (TP), accurate simulation of precipitation over the southeastern TP (SETP) is crucial for downstream moisture transport and water resource assessment. To improve SETP’s precipitation simulation accuracy, a Turbulent Orographic Form Drag (TOFD) scheme, which represents the TOFD effect of subgrid orography on model grids, is employed. The impact of TOFD on cloud and precipitation during the summer monsoon over the SETP is investigated from dynamical and cloud microphysical perspectives. Through comparison with ground observations and satellite precipitation data, TOFD reduces the simulated precipitation bias. Despite reducing moisture transport, TOFD enhances low-level convergence and high-level divergence by influencing the low-level wind, thereby strengthening updraft and cloud and precipitation processes before the slope of SETP. TOFD also increases total precipitation before the slope by promoting heavy precipitation and reducing weak precipitation. However, TOFD diminishes the updraft and cloud and precipitation processes over the slope of SETP by influencing low-level wind. The reduced moisture transport by TOFD leads to decreased precipitation over the slope. Additionally, TOFD reduces total precipitation over the slope by promoting weak precipitation and reducing heavy precipitation. Furthermore, the impact mechanism of TOFD on precipitation depends on the wind speed, which is weakened during weak precipitation period with reduced wind speed and enhanced during heavy precipitation period with increased wind speed. These findings enhance the understanding of the complex terrain's influence on cloud and precipitation and provide a basis for improving cloud and precipitation simulation over the SETP.

We revisit the issue of the response of precipitation characteristics to global warming based on analyses of global and regional climate model projections for the 21st century. The prevailing response we identify can be summarized as follows: increase in the intensity of precipitation events and extremes, with the occurrence of events of “unprecedented” magnitude, i.e., a magnitude not found in the present-day climate; decrease in the number of light precipitation events and in wet spell lengths; and increase in the number of dry days and dry spell lengths. This response, which is mostly consistent across the models we analyzed, is tied to the difference between precipitation intensity responding to increases in local humidity conditions and circulations, especially for heavy and extreme events, and mean precipitation responding to slower increases in global evaporation. These changes in hydroclimatic characteristics have multiple and important impacts on the Earth's hydrologic cycle and on a variety of sectors. As examples we investigate effects on potential stress due to increases in dry and wet extremes, changes in precipitation interannual variability, and changes in the potential predictability of precipitation events. We also stress how the understanding of the hydroclimatic response to global warming can provide important insights into the fundamental behavior of precipitation processes, most noticeably tropical convection.

Ground-based precipitation observations are sparse in the Arctic but are needed to better understand precipitation processes and to provide reference data sets for models and satellite products. This study presents new, temporally highly resolved precipitation measurements from a Pluvio precipitation gauge and a Parsivel disdrometer at the Arctic research station AWIPEV, part of the Ny-Ålesund Research Station, Svalbard. Using the information on the precipitation phase by Parsivel, we derived a temperature-dependent separation of precipitation into liquid and solid mass. The Pluvio precipitation amount and the Parsivel/temperature-based precipitation type were analyzed for the period August 2017–December 2021 and related to the presence of synoptic-scale weather systems, i.e., atmospheric rivers (ARs), cyclones and fronts, detected from ERA5 reanalysis data. ARs occurred only 8 % of the time at Ny-Ålesund but contributed to about 42 % of the total precipitation amount with a high liquid mass fraction (72 %). Cyclones occurred 20 % of the time and were associated with 39 % of the precipitation, mainly in solid form (62 %). Frontal systems play a minor role in the precipitation amount at Ny-Ålesund. Extreme events, i.e., days with daily precipitation sums above the 98th percentile, contribute 18 % to the total precipitation amount. All of these events are related to enhanced water vapor transport, often in the form of ARs and in combination with fronts and a high liquid mass fraction. Liquid precipitation in winter is mainly connected to ARs. These new measurements will help to better characterize uncertainties in gauge-based precipitation observations and the local variability of precipitation.

This study evaluates the grid-length dependency of the Weather Research and Forecasting (WRF) Model precipitation performance for two cases in the Southern Great Plains of the United States. The aim is to investigate the ability of different cumulus and microphysics parameterization schemes to represent precipitation processes throughout the transition between parameterized and resolved convective scales (e.g., the gray zone). The cases include the following: 1) a mesoscale convective system causing intense local precipitation, and 2) a frontal passage with light but continuous rainfall. The choice of cumulus parameterization appears to be a crucial differentiator in convective development and resulting precipitation patterns in the WRF simulations. Different microphysics schemes produce very similar outcomes, yet some of the more sophisticated schemes have substantially longer run times. This suggests that this additional computational expense does not necessarily provide meaningful forecast improvements, and those looking to run such schemes should perform their own evaluation to determine if this expense is warranted for their application. The best performing cumulus scheme overall for the two cases studies here was the scale-aware Grell–Freitas cumulus scheme. It was able to reproduce a smooth transition from subgrid- (cumulus) to resolved-scale (microphysics) precipitation with increasing resolution. It also produced the smallest errors for the convective event, outperforming the other cumulus schemes in predicting the timing and intensity of the precipitation.

As the atmosphere warms, part of the cloud population shifts from ice and mixed-phase (‘cold’) to liquid (‘warm’) clouds. Because warm clouds are more reflective and longer-lived, this phase change reduces the solar flux absorbed by the Earth and constitutes a negative radiative feedback. This cooling feedback is weaker in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) than in the fifth phase (CMIP5), contributing to greater greenhouse warming. Although this change is often attributed to improvements in the simulated cloud phase, another model bias persists: warm clouds precipitate too readily, potentially leading to underestimated negative lifetime feedbacks. In this study we modified a climate model to better simulate warm-rain probability and found that it exhibits a cloud lifetime feedback nearly three times larger than the default model. This suggests that model errors in cloud-precipitation processes may bias cloud feedbacks by as much as the CMIP5-to-CMIP6 climate sensitivity difference. Reliable climate model projections therefore require improved cloud process realism guided by process-oriented observations and observational constraints.CMIP6 models simulate higher and more accurate cloud liquid water fraction relative to CMIP5, but both ensembles overestimate warm cloud precipitation. Correcting these warm cloud processes in a model exposes compensating biases large enough to offset CMIP5–CMIP6 climate sensitivity differences.

By examining the historical temperature record during the industrial era, we can infer the climate's sensitivity to radiative perturbations, given knowledge of historical forcings. Energy conservation enforces a negative correlation between the climate feedback and historical forcing for a given change in global‐mean temperature. Here, we examine the negative correlation between the radiative forcing due to aerosol‐cloud interactions and the shortwave cloud feedback to warming that appears in a perturbed parameter ensemble (PPE). The PPE is not tuned to match the historical record, yet a negative correlation emerges over the extratropics due to the combined effects of liquid cloud precipitation efficiency and radiative saturation in the shortwave. Using an energy balance model, we argue that these processes combine to push Earth System Models to yield a temperature record in keeping with observations, but also limit our ability to constrain future warming posterior with the temperature record.

Subseasonal to seasonal (S2S) weather forecasting has made significant advances and several products have been made available. However, to date few studies utilize these products to extend the hydrological forecast time range. This study evaluates S2S precipitation from eight model ensembles in the hydrological simulation of extreme events at the catchment scale. A superior bias correction method is used to correct the bias of S2S precipitation for hydrological forecasts, and the results are compared with direct bias correction of hydrological forecasts using raw precipitation forecasts as input. The study shows that the S2S models can skillfully forecast daily precipitation within a lead time of 11 days. The S2S precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF), Korea Meteorological Administration (KMA), and United Kingdom’s Met Office (UKMO) models present lower mean error than that of other models and have higher correlation coefficients with observations. Precipitation data from the ECMWF, KMA, and UKMO models also perform better than that of other models in simulating multiple-day precipitation processes. The bias correction method effectively reduces the mean error of daily S2S precipitation for all models while also improving the correlation with observations. Moreover, this study found that the bias correction procedure can apply to either precipitation or streamflow simulations for improving the hydrological forecasts, even though the degree of improvement is dependent on the hydrological variables. Overall, S2S precipitation has a potential to be applied for hydrological forecasts, and a superior bias correction method can increase the forecasts’ reliability, although further studies are still needed to confirm its effect.

Climatological analyses of moisture sources of precipitation have traditionally relied on reanalyses or models that parameterize convection. Convection‐permitting models (CPMs) are increasingly used in climate studies, as they better represent many precipitation processes than non‐CPMs. We found significant differences in precipitation moisture sources over the Amazon Basin using 1‐year CPM and non‐CPM WRF simulations with moisture tracers. Notably, the CPM estimates that about half of precipitation in the central Andes comes from the Amazon basin; a 20%–30% higher estimate than the non‐CPM. This suggests long‐term CPMs with tracers could improve climatological estimates. However, their high computational cost is prohibitive. To overcome this, we developed a revised 2L‐DRM model that replicates CPM‐with‐tracers estimates at a fraction of the cost, using only standard outputs. We applied this model to South America, analyzing precipitation moisture sources across 15 regions. 2L‐DRM can be used for other regions as continental‐scale CPM climatological simulations become available.