Explore the vast range of titles available.

This study addresses the land‐atmospheric coupling strength by using long‐term AmeriFlux data from a wide range of land covers and climate regimes to reconstitute the surface exchange coefficient, Ch, which governs the total surface heat fluxes. For spring and summer, results show stronger coupling for tall canopy with Ch values ten times larger than for shorter vegetation. Observed Ch are then compared to values from the Noah land model. Results indicate that Noah underestimated (overestimated) Ch for forest (grass and crops), implying an insufficient (too efficient) coupling for tall canopy (short canopy). This discrepancy is attributed to the treatment of the roughness length for heat. With modest adjustments, the Noah model can reproduce the observed Ch. This study highlights the crucial role of treating the surface exchange processes in coupled land/weather/climate models and the need to use long‐term flux data for different vegetation types and climate regimes to assess and mitigate their deficiencies.

Intensive irrigation in the North China Plain raises significant environmental concerns. While many studies have assessed the potential irrigation impact on regional climate, most have focused solely on water addition, neglecting the indirect effects of vegetation changes. This study evaluates how vegetation dynamics influence irrigation impacts. Three experimental scenarios—dynamic vegetation (DYNM), static bare vegetation (BARE), and static lush vegetation (LUSH)—demonstrate consistent precipitation increases in April‐May‐June, but significant inconsistencies arise in July‐August‐September. Two static vegetation scenarios deviate in opposite directions from DYNM in irrigation‐induced precipitation responses: DYNM indicates a slight decrease, LUSH shows a more pronounced decrease, while BARE presents a significant increase. These discrepancies stem from different magnitudes of irrigation‐induced evapotranspiration and cooling stabilization. Furthermore, deviations in the water and energy budget may introduce uncertainties in assessing broader irrigation impacts, such as groundwater depletion, which is often studied using static vegetation assumptions. Plain Language Summary Intensive irrigation in the North China Plain raises important environmental concerns. Many studies have looked at how irrigation affects the climate, but most have simply added water to fixed vegetation and ignored the role of irrigation‐induced vegetation changes. This study examines whether using a fixed or changing vegetation would greatly affect the model‐derived conclusion on irrigation impact. We tested three scenarios: one with changing vegetation (DYNM), one with fixed and bare vegetation (BARE), and one with fixed but lush vegetation (LUSH). We found that all three scenarios showed similar increasing rainfall from April to June, but the results were quite different from July to September. The dynamic vegetation scenario showed a slight decrease in rainfall, while the lush vegetation scenario showed a larger decrease, and the bare vegetation scenario showed a significant increase. These inconsistencies is probably because irrigation triggers varying levels of moistening and cooling effect. Additionally, three scenarios have different water and energy distribution, which may have further deviations when studying how irrigation pumping affects groundwater levels. Key Points Omitting dynamic vegetation can lead to significant deviations in land‐atmosphere interactions, affecting overall model accuracy Vegetation schemes may explain inconsistencies in summer precipitation changes across studies, highlighting modeling challenges It is essential to consider vegetation‐induced indirect processes when assessing irrigation impacts on regional climate

Emissions‐driven (prognostic CO2) simulations are essential for representing two‐way carbon‐climate feedback in Earth System Models. We present an emissions‐driven land–atmosphere coupled biogeochemistry (BGC) configuration (BGCLNDATM_progCO2) in version 2.1 of the Energy Exascale Earth System Model (E3SMv2.1). This is the first E3SM configuration that performs land‐atmosphere emission‐hindcasts. Here, we document its implementation, evaluate the model's performance against observations and other models, and propose a structured evaluation protocol for such emissions‐driven simulations. We conducted transient historical simulations (1850–2014) with BGCLNDATM_progCO2 and compare them to reference simulations—a land‐atmosphere coupled simulation without BGC and a standalone land simulation with BGC, both using prescribed CO2 concentrations—and to observations. BGCLNDATM_progCO2 overestimates atmospheric CO2 concentrations by 11–23 ppm yet stays within the 40‐ppm spread CMIP6 emission‐driven models and retains physical climate properties comparable to the reference runs. The CO2 biases are partly attributed to underrepresented oceanic CO2 uptake and inadequate representations of some terrestrial processes. In general, introducing prognostic CO2 did not change physical climate metrics at the global scale but had larger regional effects, particularly over land where spatially heterogeneous CO2 and prognostic leaf area index influenced surface energy balance. Finally, we propose a general evaluation protocol including spin‐up assessment, atmospheric CO2 benchmarking, physical climate evaluation, and land biogeochemical analysis to support scientific rigor and facilitate inter‐model comparisons. The new configuration lays the groundwork for future enhancements, including improved terrestrial biogeochemical processes, integrated marine biogeochemistry, and additional human–Earth system interactions. These developments advance E3SM toward fully coupled emissions‐driven simulations, enabling more accurate carbon–climate feedback projections and informing mitigation policy by providing physically consistent carbon‐budget metrics for mitigation scenarios. Plain Language Summary Understanding the impact of carbon dioxide (CO2) emissions on climate is vital for predicting future changes and crafting effective policies. Earth System Models (ESMs) are essential tools for simulating Earth's climate and assessing various influencing factors. In this study, we extended the Energy Exascale Earth System Model (E3SM)'s capabilities so that CO2 levels are calculated directly from human and natural emissions instead of being prescribed as a single global value. This extension allows for a more realistic representation of CO2 exchange between the atmosphere and land. We conducted historical simulations from 1850 to 2014 using this new development and compared results with observations and other models. Our model slightly overestimates atmospheric CO2 levels compared to measurements but is comparable to other models in capturing key climate features. To help other researchers build and test similar “emission‐driven” models, we created a step‐by‐step evaluation framework that checks CO2 behavior, climate variables, and land‐atmosphere interactions. Our work advances E3SM modeling by accurately representing how CO2 emissions affect Earth's systems. This enhancement lays the groundwork for modeling interactions between human‐Earth interactions, thereby enabling future studies that can inform mitigation and adaption. Key Points Implemented emissions‐driven land–atmosphere biogeochemistry in E3SMv2.1 (BGCLNDATM_progCO2), enabling prognostic CO2 simulations Established a structured evaluation protocol ensuring scientific rigor and facilitating inter‐model comparisons of model performance Emissions‐driven BGCLNDATM_progCO2 simulations maintain a physical climate similar to reference runs with prescribed CO2 concentrations

To better predict and understand land–atmospheric interactions in the Tibetan Plateau (TP), we used Moderate Resolution Imaging Spectroradiometer (MODIS)-based land-use data and the MODIS-derived green vegetation fraction (GVF) to analyze the variation trend over the TP. The in situ observations from six flux stations (“BJ” (the BJ site of Nagqu Station of Plateau Climate and Environment), “MAWORS” (the Muztagh Ata Westerly Observation and Research Station), “NADORS” (the Ngari Desert Observation and Research Station), “NAMORS” (the Nam Co Monitoring and Research Station for Multisphere Interactions), “QOMS” (the Qomolangma Atmospheric and Environmental Observation and Research Station), and “SETORS” (the Southeast Tibet Observation and Research Station for the Alpine Environment)) at the Chinese TP Scientific Data Center were used to study the surface energy variation characteristics and energy distribution over different underlying surfaces. Finally, we used observation data to verify the applicability of the ERA-5 land reanalysis data to the TP. The results showed that the annual GVF steadily declined from the southeast parts to the northwest parts of the TP, and the vegetation coverage rate was highest from June to September. The sensible heat flux (H), latent heat flux (LE), net surface radiation (Rn), and four-component radiation (solar downward shortwave radiation (Rsd), surface upward shortwave radiation (Rsu), atmospheric downward longwave radiation (Rld), and surface upward longwave radiation (Rlu)) reached their maxima in summer at each station. Rld did not change significantly with time; all other variables increased during the day and decreased at night. The interannual variation in H and LE shows that latent heat exchange was the dominant form of energy transfer in BJ, MAWORS, NAMORS, and SETORS. By contrast, sensible heat exchange was the main form of energy transfer in NADORS and QOMS. The Bowen ratio was generally low in summer, and some sites had a maximum in spring. The surface albedo exhibited a “U” shape, decreasing in spring and summer, and increasing in autumn and winter, and reaching the lowest value at noon. Except for SETORS, ERA-5 Land data and other flux stations had high simulation accuracy and correlation. Regional surface energy changes were mainly observed in the eastern and western parts of the TP, except for the maximum of H in spring; the maximum values of other heat fluxes were concentrated in summer.

The coupled Weather Research and Forecasting (WRF) model with the Noah land surface model (Noah LSM) is an attempt of the modeling community to embody the complex interrelationship between land surface and atmosphere into numerical weather or climate prediction. This study describes coupled WRF/Noah model tests to evaluate the model sensitivity and improvement through vegetation fraction (Fg) parameterizations and soil moisture initialization. We utilized the 500 m 8‐day Moderate Resolution Imaging Spectroradiometer reflectance data to derive the model Fg parameter using two different methods: the linear and quadric methods. In addition, combining the Fg quadric method, we initialized soil moisture simulated by High‐Resolution Land Data Assimilation System, which has been developed for providing better soil moisture data in high spatial resolution by National Center for Atmospheric Research. We performed temporal comparisons of the simulated land surface variables: surface temperature (TS), sensible heat flux (SH), ground heat flux (GH), and latent heat flux (LH) to observed data during 2002 International H2O Project. Then these results were statistically validated with correlation coefficients and root mean square errors. The results indicate high sensitivity of the coupled model to vegetation fluctuations, showing overestimation of vegetation transpiration and very low variability of GH in highly vegetated area.