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Land-atmosphere interactions at different temporal and spatial scales are important for our understanding of the Earth system and its modeling. The Landscape Evolution Observatory (LEO) at Biosphere 2, managed by the University of Arizona, hosts three nearly identical artificial bare-soil hillslopes with dimensions of 11 × 30 m2 (1 m depth) in a controlled and highly monitored environment within three large greenhouses. These facilities provide a unique opportunity to explore these interactions. The dataset presented here is a subset of the measurements in each LEO's hillslopes, from 1 July 2015 to 30 June 2019 every 15 minutes, consisting of temperature, water content and heat flux of the soil (at 5 cm depth) for 12 co-located points; temperature, relative humidity and wind speed above ground at 5 locations and 5 different heights ranging from 0.25 m to 9–10 m; 3D wind at 1 location; the four components of radiation at 2 locations; spatially aggregated precipitation rates, total subsurface discharge, and relative water storage; and the measurements from a weather station outside the greenhouses. Measurement(s) temperature of soil • wetness of soil • heat conductivity • atmospheric wind speed • Humidity • temperature of air • longwave radiation • shortwave radiation • atmospheric wind direction • hydrological precipitation process Technology Type(s) thermocouple • dielectrometry • thermopile • anemometer • weather station • pyrgeometer • pyranometer • Flowmeter Device Factor Type(s) replicate hillslope • location • height Sample Characteristic - Environment soil • atmosphere Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12881810 [ABSTRACT FROM AUTHOR]

The article discusses the First International Noah-MP Annual Users' Workshop, which brought together over 200 participants from 16 countries to discuss advancements in the Noah-MP land surface model. The workshop focused on enhancing the model's capabilities, applicability, and interoperability in Earth system applications. The article highlights the various applications of the Noah-MP model, including weather prediction, climate projection, hydrology, agriculture, and urban heat island studies. It also identifies current challenges and limitations of the model, such as uncertainties in certain processes and a lack of communication and coordination within the Noah-MP community. The article concludes with recommendations for future model development and the establishment of a Noah-MP Academia Collaboratory to support community collaborations and enhance research-to-operation activities. [Extracted from the article]

The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as a result of leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the host weather, climate, and hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather, climate, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications. [ABSTRACT FROM AUTHOR]

Summary Previous studies have shown that the majority of the lakes in the Tibetan Plateau (TP) started to expand rapidly since the late 1990s. However, the causes are still not well known. For Nam Co, being a closed lake with no outflow, evaporation ( E L ) over the lake surface is the only way water may leave the lake. Therefore, quantifying E L is key for investigating the mechanism of lake expansion in the TP. E L can be quantified by Penman- and/or bulk-transfer-type models, requiring only net radiation, temperature, humidity and wind speed for inputs. However, interpolation of wind speed data may be laden with great uncertainty due to extremely sparse ground meteorological observations, the highly heterogeneous landscape and lake-land breeze effects. Here, evaporation of Nam Co Lake was investigated within the 1979–2012 period at a monthly time-scale using the complementary relationship lake evaporation (CRLE) model which does not require wind speed data. Validations by in-situ observations of E601B pan evaporation rates at the shore of Nam Co Lake as well as measured E L over an adjacent small lake using eddy covariance technique suggest that CRLE is capable of simulating E L well since it implicitly considers wind effects on evaporation via its vapor transfer coefficient. The multi-year average of annual evaporation of Nam Co Lake is 635 mm. From 1979 to 2012, annual evaporation of Nam Co Lake expressed a very slight decreasing trend. However, a more significant decrease in E L occurred during 1998–2008 at a rate of −12 mm yr −1 . Based on water-level readings, this significant decrease in lake evaporation was found to be responsible for approximately 4% of the reported rapid water level increase and areal expansion of Nam Co Lake during the same period. [ABSTRACT FROM AUTHOR]

This study is intended to simulate and better understand mean and extreme precipitation over the Loess Plateau (LP) in China using the Weather Research and Forecasting (WRF) model. We performed a long-term (1980–2011) simulation with the WRF model at a 10 km horizontal resolution, forced by the ERA-Interim reanalysis. A series of sensitivity experiments were conducted to investigate the influence of resolution and physical parameterization schemes on the simulation ability of the WRF model over the LP. The region has a dominant semi-arid climate and is adversely affected by extreme precipitation. Results show that the WRF model produced better simulations at a 10 km resolution for the LP with complex terrain than it did at coarser resolutions. WRF simulations for precipitation over the LP were most sensitive to cumulus schemes, moderately sensitive to planetary boundary layer schemes, and less sensitive to microphysics schemes. The WRF model not only adequately captured the spatial distribution of precipitation in the LP but also reproduced its magnitude and variability at different time scales. Moreover, the WRF model captured 73% of the observed extreme precipitation events with an intensity of more than 12 mm day− 1, which cause substantial soil erosion in the LP. Although it was unable to capture 27% of the observed events, the WRF model reasonably reproduced ERA-Interim extreme precipitation and its dynamic processes. These analyses strongly suggest that the biased extreme precipitation generated with the WRF model most likely resulted from inaccurate ERA-Interim forcing data. This study provides a better understanding of extreme precipitation simulations as well as insight into predictions of soil erosion in the LP. [ABSTRACT FROM AUTHOR]

This study developed an improved vegetation emissivity scheme for the Community Land Model (CLM) version 4.5 to more accurately simulate the effects of vegetation emissivity on snow processes in the Northern Hemisphere over winter and spring. The original scheme of vegetation emissivity in CLM produced an unreasonably low vegetation emissivity with a minimum value of around 0.70 in the cold season. Thus, we developed a new vegetation emissivity scheme based on maximum emissivity and leaf and stem area indices of vegetation, which can simulate vegetation emissivity more realistically than the original scheme. Our simulations were driven by the Climatic Research Unit-National Centers for Environmental Prediction (CRU-NCEP) reanalysis data. Results show that CLM with the new scheme produces stronger longwave radiation to the ground surface and generates more solid water drips off vegetation over winter and spring than with the original scheme. Such changes improve snow cover fraction (SCF) simulations for the middle and high latitudes in North America, central Eurasia, and the eastern Tibetan Plateau. About 200 and 350 thousand km2 with the SCF changes show a better SCF simulation with the new scheme over winter and spring, respectively. However, increased errors were found in SCF simulations with the new scheme, and further analysis indicates that such errors may be related to biases in the CLM forcing variables from the CRU-NCEP reanalysis data as compared with those from in situ observations. Moreover, the new emissivity scheme decreases total upward longwave radiation and increases surface net radiation and turbulent fluxes. Overall, the improved vegetation emissivity scheme in this study provides an effective tool to generate better understanding of the effects of vegetation on snow at regional scales and gives strong insight into improved land surface process modeling. [ABSTRACT FROM AUTHOR]

This paper defines a new scoring rule, namely relative model score (RMS), for evaluating ensemble simulations of environmental models. RMS implicitly incorporates the measures of ensemble mean accuracy, prediction interval precision, and prediction interval reliability for evaluating the overall model predictive performance. RMS is numerically evaluated from the probability density functions of ensemble simulations given by individual models or several models via model averaging. We demonstrate the advantages of using RMS through an example of soil respiration modeling. The example considers two alternative models with different fidelity, and for each model Bayesian inverse modeling is conducted using two different likelihood functions. This gives four single-model ensembles of model simulations. For each likelihood function, Bayesian model averaging is applied to the ensemble simulations of the two models, resulting in two multi-model prediction ensembles. Predictive performance for these ensembles is evaluated using various scoring rules. Results show that RMS outperforms the commonly used scoring rules of log-score, pseudo Bayes factor based on Bayesian model evidence (BME), and continuous ranked probability score (CRPS). RMS avoids the problem of rounding error specific to log-score. Being applicable to any likelihood functions, RMS has broader applicability than BME that is only applicable to the same likelihood function of multiple models. By directly considering the relative score of candidate models at each cross-validation datum, RMS results in more plausible model ranking than CRPS. Therefore, RMS is considered as a robust scoring rule for evaluating predictive performance of single-model and multi-model prediction ensembles. [ABSTRACT FROM AUTHOR]

One of the recognized weaknesses of land surface models as used in weather and climate models is the assumption of constant soil thickness because of the lack of global estimates of bedrock depth. Using a 30-arc-s global dataset for the thickness of relatively porous, unconsolidated sediments over bedrock, spatial variation in soil thickness is included here in version 4.5 of the Community Land Model (CLM4.5). The number of soil layers for each grid cell is determined from the average soil depth for each 0.9° latitude × 1.25° longitude grid cell. The greatest changes in the simulation with variable soil thickness are to baseflow, with the annual minimum generally occurring earlier. Smaller changes are seen in latent heat flux and surface runoff primarily as a result of an increase in the annual cycle amplitude. These changes are related to soil moisture changes that are most substantial in locations with shallow bedrock. Total water storage (TWS) anomalies are not strongly affected over most river basins since most basins contain mostly deep soils, but TWS anomalies are substantially different for a river basin with more mountainous terrain. Additionally, the annual cycle in soil temperature is partially affected by including realistic soil thicknesses resulting from changes in the vertical profile of heat capacity and thermal conductivity. However, the largest changes to soil temperature are introduced by the soil moisture changes in the variable soil thickness simulation. This implementation of variable soil thickness represents a step forward in land surface model development. [ABSTRACT FROM AUTHOR]

Zero-order drainage basins, and their constituent hillslopes, are the fundamental geomorphic unit comprising much of Earth's uplands. The convergent topography of these landscapes generates spatially variable substrate and moisture content, facilitating biological diversity and influencing how the landscape filters precipitation and sequesters atmospheric carbon dioxide. In light of these significant ecosystem services, refining our understanding of how these functions are affected by landscape evolution, weather variability, and long-term climate change is imperative. In this paper we introduce the Landscape Evolution Observatory (LEO): a large-scale controllable infrastructure consisting of three replicated artificial landscapes (each 330 m 2 surface area) within the climate-controlled Biosphere 2 facility in Arizona, USA. At LEO, experimental manipulation of rainfall, air temperature, relative humidity, and wind speed are possible at unprecedented scale. The Landscape Evolution Observatory was designed as a community resource to advance understanding of how topography, physical and chemical properties of soil, and biological communities coevolve, and how this coevolution affects water, carbon, and energy cycles at multiple spatial scales. With well-defined boundary conditions and an extensive network of sensors and samplers, LEO enables an iterative scientific approach that includes numerical model development and virtual experimentation, physical experimentation, data analysis, and model refinement. We plan to engage the broader scientific community through public dissemination of data from LEO, collaborative experimental design, and community-based model development. [ABSTRACT FROM AUTHOR]

• Recent significant terrestrial drying and wetting are explained in terms of climatic forcing using partial least-square regression models. • LAI, downward longwave radiation (DLW) and precipitation are most strongly associated with a wetting trend. • Enhanced atmospheric demand brought about by air temperature and DLW dominates a drying trend. Terrestrial water storage (TWS) experienced a substantial change in the past few decades as detected by the Gravity Recovery and Climate Experiment (GRACE). However, the major causes of this change remain unclear, and none of the current state-of-the-art process-based hydrological models are able to reproduce the significantly drying/wetting trends in GRACE TWS. Here we investigate 12 terrestrial regions that show a significantly drying/wetting trend, using partial least-square regression (PLSR) to relate TWS anomalies to various climatic variables and leaf area index (LAI). Through PLSR modeling, we find changes in LAI, downward longwave radiation (DLW) and precipitation are most strongly associated with a wetting trend. Increases in precipitation appear to be responsible for the wetting trend in tropical/subtropical monsoon regions, while decreases in vegetation transpiration and atmospheric demand appear to be responsible for the wetting trend in extratropical regions. Enhanced atmospheric demand caused by increases in air temperature and the resulting enhanced DLW dominates the significant drying trend in the mid-latitude subtropical drylands, and in the cold and alpine regions. The PLSR modeling also suggests that, over global continents, the climatic forcing factors show a dominant impact on TWS over all the wetting regions, while only four out of the seven drying regions show climate-dominated drying, implying an additional impact of anthropogenic responses to the water stress on drying. [ABSTRACT FROM AUTHOR]