The ICON‐ParFlow Coupling: Integrating a Continental‐Scale Hydrological Model Into a Global Atmospheric Model

Role of land surface processes in convection using the WRF model: a high-resolution case study over the Indian subcontinent

Impacts of soil–aquifer heat and water fluxes on simulated global climate

The Response of Tropical Land-Ocean Precipitation Partitioning to SST and CO2 Increase in Global Storm-Resolving Simulations

The tropical land-ocean precipitation partitioning is skewed toward the land. This study assesses how CO 2 increase and uniform sea surface temperature increase affect this partitioning. We analyze 14-year global simulations conducted with the ICON model at 10-km horizontal grid spacing, at which convection is simulated explicitly. ICON’s precipitation partitioning shows better agreement with observations than the AMIP6 ensemble. Under 4×CO 2 , precipitation partitioning increases, favoring land precipitation, whereas it decreases upon +4K SSTs. We develop a diagnostic framework based on the land’s atmospheric energy and moisture budgets to decompose the response of tropical precipitation partitioning into contributions from land atmospheric heating, land circulation efficiency, land moisture cycling, and tropical radiative cooling. In ICON and the AMIP6 ensemble, the land atmospheric heating is identified as the primary controlling factor for changes in precipitation partitioning. Changes in atmospheric heating drive circulation adjustments that modulate land precipitation through changes in land moisture convergence. The response of the controlling factors is similar in ICON and in the AMIP6 ensemble, apart from two qualitative differences. First, the controlling factors are generally more stable toward the imposed forcings in ICON compared to the AMIP6 ensemble. Second, the opposing responses in precipitation partitioning upon CO 2 forcing and equivalent uniform SST increase are of virtually equal magnitude in ICON, whereas in AMIP6 precipitation partitioning responds more strongly to the uniform SST increase. These findings suggest that coarse-resolution models may underestimate the land hydrological sensitivity relative to the total tropical hydrological sensitivity.

A Zonal‐Meridional Projection Method for Quantifying Global Land‐Ocean Moisture Transport

Accurate quantification of moisture transport between the ocean and the land is essential for understanding the global water and energy cycles. Existing methods based on flux divergence or trajectory analysis are computationally demanding and not well suited to irregular coastlines. Here we develop a zonal‐meridional projection (ZMP) method that directly measures water‐moisture fluxes across global coastal interfaces. Vertically integrated moisture fluxes derived from ERA5 reanalysis (0.25°, 1979–2020) are projected onto coastal‐normal directions and multiplied by a signed land‐side boundary length derived from sub‐segmented grid edges. This line‐integral formulation preserves the physical consistency of the flux direction and automatically distinguishes inflow and outflow through the signs of wind and coastline orientation. It also improves computational efficiency by avoiding conditional checks of wind direction or coastal type. The results reveal coherent ocean‐to‐land inflow corridors along monsoonal and intertropical regions and a statistically significant upward trend in total inflow since 1979, consistent with an intensifying global hydrological cycle. The proposed ZMP method provides a geometrically precise, scalable and computationally efficient approach for quantifying land‐ocean moisture exchange in both reanalysis and climate‐model applications.

Enhanced seasonal soil moisture forecasts by integrating APCC multi-model ensemble predictions into a land surface model framework

Soil moisture is a key driver of climate variability and extremes such as heatwaves and wildfires, and accurate prediction at seasonal timescales is therefore essential. Skillful forecasts at these scales, however, remain a significant challenge. While multi-model ensemble (MME) climate forecasts consistently outperform individual models for atmospheric variables, their soil moisture outputs require careful interpretation when combined across models because of differences in land surface model (LSM) structures and soil configurations. To bridge this gap, we developed a seasonal soil moisture prediction system using the Joint UK Land Environment Simulator driven by NCEP CFSv2 meteorological forecasts, and integrated monthly temperature and precipitation forecasts from the APCC MME—which exhibits superior seasonal prediction skill to single dynamical models—into the system’s meteorological forcing. Despite correcting only temperature and precipitation—the two variables consistently available across all participating MME models—the approach yielded substantial improvements in soil moisture forecast skill. Retrospective hindcast experiments for 1991–2016, initialized in February, show that the MME-corrected forecast (J-MME) substantially outperforms the CFSv2-driven baseline (J-CFSv2) in predicting boreal spring–summer soil moisture. The global average coefficient of determination (R2) between forecast and reanalysis increased by 0.10–0.12 at three- to four-month lead times. The improvements were driven by precipitation correction in water-limited regions and temperature correction in energy-limited high-latitude regions. This approach also extended the effective lead time for statistically significant predictions and improved the detection of historical drought events, demonstrating that even limited integration of MME information into an LSM framework can yield meaningful gains in seasonal soil moisture and drought forecasting.

Integrated analysis of surface water-groundwater interactions and enhanced hydrological forecasting of glacierized river in cold and arid regions

In cold and arid regions, climate change has altered precipitation patterns and accelerated glacierized retreat, which imposes severe risks on the stability of regional hydrological systems. Combined with the strong interactions between surface water (SW) and groundwater (GW) regulated by distinctive geological conditions, clarifying the baseline hydrological regime and predicting future hydrological changes is critical to maintaining local ecosystem stability. The headwater catchment of the Bortala River in Northwest China represents a typical glacierized watershed dominated by spring-fed discharge. Under the continuous influence of climate change, quantitative investigations into surface water-groundwater interactions in this catchment are still inadequate. In this study, a coupled SWAT-MODFLOW model embedded with a glacier module was developed to clarify the surface water-groundwater interactions relationship. Based on the Budyko framework, under CMIP6 climate scenarios, to establish an optimized decomposition and reconstruction simulation system, furthermore, to explore the changing characteristics of temperature, precipitation, and runoff. Observed hydrometeorological parameters were utilized to calibrate and validate the established model. A set of statistical indices was employed to assess the modeling performance, and the coupled model attained favorable simulation accuracy. The main results indicated that although surface water served as the dominant recharge source for groundwater from 1971 to 2020, representing a cumulative reduction of 26.7% from the initial value of the historical period, and an increased average annual decline rate of 0.45%. Future scenario projections revealed an increasing trend in total watershed runoff under both SSP2-4.5 and SSP5-8.5, with growth rates of 13.6% and 14.2%, respectively. A runoff inflection point is projected to occur around the 2070s (± 10 years), which will appear approximately 10 years earlier under the high-emission SSP5-8.5 scenario. In addition, intensified precipitation extremes may trigger severe hydrological risks, which are expected to concentrate around the 2040s and 2070s. The SWAT-MODFLOW model quantifies surface water-groundwater interactions in the glacial-snowmelt recharge system of the spring river, advancing hydrological modelling in cold-arid regions. Within the Budyko framework, the modified Choudhury-Yang equation combines temperature change and glacial-snowmelt recharge to analyze the spring river. CMIP6-driven SWAT-MODFLOW enhances runoff forecasting for the spring river. Combined with the SSP2-4.5 and SSP5-8.5 scenarios, it predicts future variations in temperature, precipitation, and runoff, identifies hydrological change inflection points, and provides a basis for flood prevention and water resources management. The SWAT-MODFLOW model quantifies surface water-groundwater interactions in the glacial-snowmelt recharge system of the spring river, advancing hydrological modelling in cold-arid regions. Within the Budyko framework, the modified Choudhury-Yang equation combines temperature change and glacial-snowmelt recharge to analyze the spring river. CMIP6-driven SWAT-MODFLOW enhances runoff forecasting for the spring river. Combined with the SSP2-4.5 and SSP5-8.5 scenarios, it predicts future variations in temperature, precipitation, and runoff, identifies hydrological change inflection points, and provides a basis for flood prevention and water resources management.