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Abstract Non-technical summary Greenhouse gas emissions and land use change – from deforestation, forest degradation, and agricultural intensification – are contributing to climate change and biodiversity loss. Important land-based strategies such as planting trees or growing bioenergy crops (with carbon capture and storage) are needed to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS) is an international knowledge-exchange and capacity-building network, specializing in ecosystems and their role in controlling the exchange of water, energy and chemical compounds between the land surface and the atmosphere. We outline priority directions for land–atmosphere interaction research and its contribution to the sustainable development agenda. Technical summary Greenhouse-gas emissions from human activities and land use change (from deforestation, forest degradation, and agricultural intensification) are contributing to climate change and biodiversity loss. Afforestation, reforestation, or growing bioenergy crops (with carbon capture and storage) are important land-based strategies to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The effectiveness of these actions depends on terrestrial ecosystems and their role in controlling or moderating the exchange of water, heat, and chemical compounds between the land surface and the atmosphere. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS), a global research network of Future Earth, enables the international community to communicate and remain up to date with developments and concepts about terrestrial ecosystems and their role in global water, energy, and biogeochemical cycles. Covering critically important topics such as fire, forestry, wetlands, methane emissions, urban areas, pollution, and climate change, the iLEAPS Global Research Programme sits center stage for some of the most important environmental questions facing humanity. In this paper, we outline the new challenges and opportunities for land–atmosphere interaction research and its role in supporting the broader sustainable development agenda. Social Media Summary Future directions for research into land–atmosphere interactions that supports the sustainable development agenda

Abstract In this editorial, we present general principles as well as specific notions that guide the strategy of JAMES′ editors in realizing the journal's mission.

As the editors of a scientific journal, we have the honor of front‐row seats to the peer review process. We watch as reviewers bring fresh perspectives to authors' work, identifying weakness in conception or expression, suggesting refinements, and urging authors to clarify a point or better support an argument. The process is occasionally uncomfortable but almost always respectful and constructive. The final papers are uniformly better for this input—a reminder of the power of collaboration. At the Journal of Advances in Modeling Earth Systems (JAMES), we are fortunate to have a pool of reviewers who volunteer their time, knowledge, and intelligence to improve the work of their colleagues and peers. It is no small task: In 2020, we received 956 reviews by 634 individuals. Speaking for ourselves, the 19 devoted associate editors, and many of our authors, we thank each of you for your selfless contributions to strengthening our community.

Key Points The editors thank the 2019 peer reviewers

Abstract As the editors of a scientific journal, we are privileged to have box seats to the peer review process. We watch as reviewers shine a new light on authors' work, identifying weakness in conception or expression, suggesting refinements, urging authors to clarify a point or better support an argument. The process is occasionally uncomfortable but it is almost always respectful and constructive and papers are uniformly better for this input. At the Journal of Advances in Modeling Earth Systems (JAMES) we are fortunate to have a pool of reviewers who volunteer their time, insight, knowledge, and intelligence to improve the work of their colleagues and peers. It is no small task: In 2018, we received 715 reviews by 494 individuals. On behalf of the entire editorial board and our authors we thank you all for your selfless contributions to strengthening our community.

The authors wish to make the following corrections to this paper [...]

The persistence or memory of soil moisture (θ) after rainfall has substantial environmental implications. Much work has been done to study soil moisture drydown for in situ and satellite data separately. In this work, we present a comparison of drydown characteristics across multiple U.K. soil moisture products, including satellite-merged (i.e., TCM), in situ (i.e., COSMOS-UK), hydrological model [i.e., Grid-to-Grid (G2G)], statistical model [i.e., Soil Moisture U.K. (SMUK)], and land surface model (LSM) [i.e., Climate Hydrology and Ecology research Support System (CHESS)] data. The drydown decay time scale (τ) for all gridded products is computed at an unprecedented resolution of 1–2 km, a scale relevant to weather and climate models. While their range of τ differs (except SMUK and CHESS are similar) due to differences such as sensing depths, their spatial patterns are correlated to land cover and soil types. We further analyze the occurrence of drydown events at COSMOS-UK sites. We show that soil moisture drydown regimes exhibit strong seasonal dependencies, whereby the soil dries out quicker in summer than winter. These seasonal dependencies are important to consider during model benchmarking and evaluation. We show that fitted τ based on COSMOS and LSM are well correlated, with a bias of lower τ for COSMOS. Our findings contribute to a growing body of literature to characterize τ, with the aim of developing a method to systematically validate model soil moisture products at a range of scales. Significance Statement: While important for many aspects of the environment, the evaluation of modeled soil moisture has remained incredibly challenging. Sensors work at different space and time scales to the models, the definitions of soil moisture vary between applications, and the soil moisture itself is subject to the soil properties while the impact of the soil moisture on evaporation or river flow is more dependent on its variation in time and space than its absolute value. What we need is a method that allows us to compare the important features of soil moisture rather than its value. In this study, we choose to study drydown as a way to capture and compare the behavior of different soil moisture data products. [ABSTRACT FROM AUTHOR]

Efficient and continuous monitoring of surface water is essential for water resource management. Much effort has been devoted to the task of water mapping based on remote sensing images. However, few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner. Moreover, water area statistics are sensitive to the satellite image observation quality. This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine (GEE) with a supervised random forest classifier. A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products. The samples for permanent and seasonal water were mapped and collected separately, so that the supplement of seasonal samples can increase the spectral diversity of the sample space. To reduce the uncertainty of the derived water occurrences, temporal correction was applied to repair the classification maps with invalid observations. Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results. Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods, as well as the GSWD and GLAD datasets. [ABSTRACT FROM AUTHOR]

Carbon isotope discrimination (Δ13C) in C3 woody plants is a key variable for the study of photosynthesis. Yet how Δ13C varies at decadal scales, and across regions, and how it is related to gross primary production (GPP), are still incompletely understood. Here we address these questions by implementing a new Δ13C modelling capability in the land‐surface model JULES incorporating both photorespiratory and mesophyll‐conductance fractionations. We test the ability of four leaf‐internal CO2 concentration models embedded in JULES to reproduce leaf and tree‐ring (TR) carbon isotopic data. We show that all the tested models tend to overestimate average Δ13C values, and to underestimate interannual variability in Δ13C. This is likely because they ignore the effects of soil water stress on stomatal behavior. Variations in post‐photosynthetic isotopic fractionations across species, sites and years, may also partly explain the discrepancies between predicted and TR‐derived Δ13C values. Nonetheless, the "least‐cost" (Prentice) model shows the lowest biases with the isotopic measurements, and lead to improved predictions of canopy‐level carbon and water fluxes. Overall, modelled Δ13C trends vary strongly between regions during the recent (1979–2016) historical period but stay nearly constant when averaged over the globe. Photorespiratory and mesophyll effects modulate the simulated global Δ13C trend by 0.0015 ± 0.005‰ and –0.0006 ± 0.001‰ ppm−1, respectively. These predictions contrast with previous findings based on atmospheric carbon isotope measurements. Predicted Δ13C and GPP tend to be negatively correlated in wet‐humid and cold regions, and in tropical African forests, but positively related elsewhere. The negative correlation between Δ13C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ13C in those forests. Our results demonstrate that the combined analysis of Δ13C and GPP can help understand the drivers of photosynthesis changes in different climatic regions. [ABSTRACT FROM AUTHOR]

The Noah‐MP land surface model (LSM) relies on the Monin‐Obukhov (M‐O) Similarity Theory (MOST) to calculate land‐atmosphere exchanges of water, energy, and momentum fluxes. However, MOST flux‐profile relationships neglect canopy‐induced turbulence in the roughness sublayer (RSL) and parameterize within‐canopy turbulence in an ad hoc manner. We implement a new physics scheme (M‐O‐RSL) into Noah‐MP that explicitly parameterizes turbulence in RSL. We compare Noah‐MP simulations employing the M‐O‐RSL scheme (M‐O‐RSL simulations) and the default M‐O scheme (M‐O simulations) against observations obtained from 647 Snow Telemetry (SNOTEL) stations and two AmeriFlux stations in the western United States. M‐O‐RSL simulations of snow water equivalent (SWE) outperform M‐O simulations over 64% and 69% of SNOTEL sites in terms of root‐mean‐square‐error (RMSE) and correlation, respectively. The largest improvements in skill for M‐O‐RSL occur over closed shrubland sites, and the largest degradations in skill occur over deciduous broadleaf forest sites. Differences between M‐O and M‐O‐RSL simulated snowpack are primarily attributable to differences in aerodynamic conductance for heat underneath the canopy top, which modulates sensible heat flux. Differences between M‐O and M‐O‐RSL within‐canopy and below‐canopy sensible heat fluxes affect the amount of heat transported into snowpack and hence change snowmelt when temperatures are close to or above the melting point. The surface energy budget analysis over two AmeriFlux stations shows that differences between M‐O and M‐O‐RSL simulations can be smaller than other model biases (e.g., surface albedo). We intend for the M‐O‐RSL physics scheme to improve performance and uncertainty estimates in weather and hydrological applications that rely on Noah‐MP. Plain Language Summary: Most widely used computer models of the land surface neglect canopy‐induced turbulence in calculations of heat, water, and momentum exchanges. Accounting for canopy‐induced turbulence is important because coherent eddies that form near the canopy top are responsible for generating most of the transportation of heat, water, and momentum, in and directly above the canopy. In 2007, 2008 scientists Ian N. Harman and John J. Finnigan developed a methodology that adapts contemporarily used equations in operational LSMs to account for canopy‐induced turbulence. In this study, we create a new physics option for the Noah with Multi‐Parameterization (Noah‐MP) LSM that accounts for canopy‐induced turbulence based on the Harman and Finnigan, 2007–2008 methodology. The primary focus of this study is to quantify differences between Noah‐MP snowpack simulations using the classical physics option that neglects canopy‐induced turbulence with the new physics option that accounts for canopy‐induced turbulence. Overall, simulations using the new physics option tend to have better agreement with ground‐based SWE observations across 647 validation sites within the western United States. We intend for the new physics scheme to improve weather and hydrological applications for operational modeling systems that rely on the Noah‐MP LSM. Key Points: This study presents a new physics option for Noah‐MP that accounts for canopy‐induced turbulence in the roughness sublayerSimulated SWE over shrublands using the new turbulence scheme agree more with observations than those using the traditional MOST schemeDifferences in snow simulations are mainly attributable to differences in aerodynamic resistance to sensible heat below the canopy top [ABSTRACT FROM AUTHOR]

The COSMOS-UK observation network has been providing field-scale soil moisture and hydrometeorological measurements across the UK since 2013. At the time of publication a total of 51 COSMOS-UK sites have been established, each delivering high-temporal resolution data in near-real time. Each site utilizes a cosmic-ray neutron sensor, which counts epithermal neutrons at the land surface. These measurements are used to derive field-scale near-surface soil water content, which can provide unique insight for science, industry, and agriculture by filling a scale gap between localized point soil moisture and large-scale satellite soil moisture datasets. Additional soil physics and meteorological measurements are made by the COSMOS-UK network including precipitation, air temperature, relative humidity, barometric pressure, soil heat flux, wind speed and direction, and components of incoming and outgoing radiation. These near-real-time observational data can be used to improve the performance of hydrological models, validate remote sensing products, improve hydro-meteorological forecasting, and underpin applications across a range of other scientific fields. The most recent version of the COSMOS-UK dataset is publically available at 10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185 (Stanley et al., 2021). [ABSTRACT FROM AUTHOR]

In this study, we implement a new frozen‐soil parameterization scheme into the climate system model CAS‐FGOALS‐g3 to investigate the dynamic changes of freezing and thawing fronts and the effects arising from thermal processes and climate. Simulations are conducted using the developed model to validate its performance relative to multi‐source observations. It is shown that the model could reasonably reproduce soil freezing and thawing processes, including dynamic changes in freezing and thawing fronts. The historical simulation shows that the maximum freeze depth increases with an increase of latitude in seasonally frozen ground, and the active layer thickness decreases with an increase of latitude in permafrost regions. The active layer thickness shows increasing trends while the maximum freeze depth shows decreasing trends, which is consistent with change in the 2‐m air temperature. In conclusion, these results have the potential to further deepen our understanding of the freeze‐thaw cycle process and the historical response of permafrost to climate change. Plain Language Summary: The dynamic changes of freezing and thawing fronts have a significant influence on the surface energy balance, hydrological processes, cold‐zone engineering practices and the terrestrial carbon cycle. In this study, a frozen‐ground algorithm that considers the dynamic changes of freezing and thawing fronts is implemented in a global climate system model to investigate the dynamic changes of freezing and thawing fronts and the effects arising from thermal processes and climate. It is shown that the developed model can reasonably reproduce soil freezing and thawing processes, including changes in freezing and thawing fronts. The active layer thickness shows increasing trends, and the maximum freeze depth shows decreasing trends. These results have the potential to further deepen our understanding of the freeze‐thaw cycle process and the historical response of permafrost. Key Points: A new frozen soil algorithm, including freezing and thawing fronts, is implemented in the climate model CAS‐FGOALS‐g3The updated model can capture changes of freezing and thawing fronts, which affects soil moisture and soil temperatureThe active layer thickness shows increasing trends, while the maximum freeze depth shows decreasing trends [ABSTRACT FROM AUTHOR]

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade‐off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade‐off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers. Plain Language Summary: Global climate models must accurately represent many processes, including the rate at which plants convert sunlight, water, and CO2 into sugar. The enormous Amazon rainforest is extremely biologically productive, so the region strongly influences global CO2 cycling. Measurements from instruments on towers in the Amazon, despite imperfections, seem to provide the most accurate estimates of rainforest plant productivity rates that exist. We compare tower estimates to 18 global models, focusing on subtle dry versus wet rainforest seasons. Generically, reality is more variable than models simulate. If a model accurately represents changes to an outcome when predictor variables change, predictions will have a narrower spread than is realistic. Accuracy errors can overwhelm the smoothing tendency, however. Models that add random uncertainty are an alternative in some simple situations, but the tendency toward narrowed predictions remains problematic for climate models. Modeled monthly plant productivity poorly matches tower estimates. About half the models have stronger seasonal cycles than the towers and half have less. By mathematical logic, the models with high seasonal spread must be overly responsive to inputs. As expected, the strongly seasonal models simulate greater change in productivity in response to variations in rain amounts than tower data suggest is realistic. Key Points: Regression logic is reason to doubt the accurate climate sensitivity of predictions whose variability is realistic or higherA suite of models poorly reproduces eddy covariance estimates of Amazon rainforest gross primary productivityHighly seasonal models predict stronger primary productivity responsiveness to meteorology than is likely to be true [ABSTRACT FROM AUTHOR]

In this study, the impact of gravel was taken into account in the regional climate model version 4.7, which was driven by the ERA‐Interim with a resolution of 1.5 km in this numerical experiment. The gravel parameterization schemes and the original soil hydrothermal parameterization schemes were used in the regional soil hydrothermal simulations on the Tibetan Plateau (TP), respectively. The performance of the two schemes in simulating the soil moisture (SM) was assessed. Moreover, three areas were selected in terms of the distribution of gravel content on the TP to investigate the differences in the internal mechanism of the model between the two schemes. The performance of the gravel schemes in simulating SM is generally improved compared with that of the original schemes. The simulation effect is superior in the southeastern TP. Except for the southeastern TP, the performance in simulating the temporal variations of SM below the soil depth of 1.0 m still needs to be improved. The fractional saturated area (fsat) and the fraction of the inundated area (fh2osfc) are two pivotal variables that control the important component of the hydrologic process. There may be a threshold for the increase of fh2osfc, when the increase of fh2osfc is lower than this value, the main effect of increasing fh2osfc is to reduce the surface moisture that is, divided into the input of soil. However, when the increase of fh2osfc is higher than this value, the main effect of increasing fh2osfc is to increase the bottom drainage from the surface water store. The increasing gravel content will induce the decreasing mineral soil saturated hydraulic conductivity, which leads to SM increases, the opposite is true. Plain Language Summary: Often referred to as the "Third pole" or the "water Tower of Asia," the Tibetan Plateau (TP) is the source region of major rivers in Asia. Its hydrological processes exert a great impact on the Ecological environment and the Socio‐economic development of the watershed and the lower reaches. Therefore, it is necessary to understand the change regularity and mechanism in the hydrological processes of the TP. But limited by the environmental conditions, the in‐situ observations used in the research of the TP are very scarce, thus it is an effective complementary approach to investigate the hydrological processes of the TP using the land surface or hydrological models that are vigorously evaluated. However, there are many problems in the land surface models coupled into the climate models concerning the parameterizations of the TP soil, one of them is ignoring the gravel which widely distributed in the TP. In this study, the impact of gravel is taken into account in regional simulations on the TP, the results show that the accuracy of the simulation has been improved significantly. Key Points: The impact of gravel is taken into account in the regional soil hydrothermal simulation on the Tibetan PlateauThe gravel schemes generally perform better than the original schemes in reproducing the soil moisture on the Tibetan Plateau [ABSTRACT FROM AUTHOR]

Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the "soil14_psi" experiments), when the critical threshold value for inducing soil moisture stress was reduced ("soil14_p0"), and when plants were able to access soil moisture in deeper soil layers ("soil14_dr*2"). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes. [ABSTRACT FROM AUTHOR]

Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections. [ABSTRACT FROM AUTHOR]

Key Points: The editors thank the 2019 peer reviewers [ABSTRACT FROM AUTHOR]

Spinning up a highly complex, coupled Earth system model (ESM) is a time consuming and computationally demanding exercise. For models with interactive ice sheet components, this becomes a major challenge, as ice sheets are sensitive to bidirectional feedback processes and equilibrate over glacial timescales of up to many millennia. This work describes and demonstrates a computationally tractable, iterative procedure for spinning up a contemporary, highly complex ESM that includes an interactive ice sheet component. The procedure alternates between a computationally expensive coupled configuration and a computationally cheaper configuration where the atmospheric component is replaced by a data model. By periodically regenerating atmospheric forcing consistent with the coupled system, the data atmosphere remains adequately constrained to ensure that the broader model state evolves realistically. The applicability of the method is demonstrated by spinning up the preindustrial climate in the Community Earth System Model Version 2 (CESM2), coupled to the Community Ice Sheet Model Version 2 (CISM2) over Greenland. The equilibrium climate state is similar to the control climate from a coupled simulation with a prescribed Greenland ice sheet, indicating that the iterative procedure is consistent with a traditional spin‐up approach without interactive ice sheets. These results suggest that the iterative method presented here provides a faster and computationally cheaper method for spinning up a highly complex ESM, with or without interactive ice sheet components. The method described here has been used to develop the climate/ice sheet initial conditions for transient, ice sheet‐enabled simulations with CESM2‐CISM2 in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Plain Language Summary: Experiments with Earth system models typically use the preindustrial (1850 CE) climate as a reference point when examining the climate response to a given experiment scenario. A preindustrial simulated climate state is therefore important to develop and represent consistently, which often requires long and computationally expensive spin‐up or equilibration simulations. The latest generation Earth system models include time‐evolving ice sheet components, which complicate the task of generating a self‐consistent simulated preindustrial climate. Additional complexity arises because ice sheets interact with the rest of the climate system through complex processes and feedbacks and respond slowly to climate change over many millennia. This equilibration timescale is computationally intractable using traditional spin‐up/equilibration simulation techniques. To circumvent this challenge, we present a novel method for generating an internally consistent climate state that is suitable for models with interactive ice sheet components. This method uses fewer computational resources than traditional simulation methods, while generating a climate consistent with more expensive methods. We demonstrate the viability of the method by generating the preindustrial control climate in the Community Earth System Model Version 2 (CESM2), which includes an interactive Greenland ice sheet. Key Points: We describe a computationally tractable, iterative procedure for spinning up a coupled Earth system‐ice sheet modelEquilibrium state from the iterative procedure is similar to a more expensive traditional model spin‐up with prescribed ice sheetsThe procedure is used for developing initial conditions for transient, fully coupled simulations in the Coupled Model Intercomparison Project phase 6 [ABSTRACT FROM AUTHOR]

Hydrology is still, and for good reasons, an inexact science (for a recent discussion, see Beven, [4]), even if evolving hydrological understanding has provided a basis for improved water management for at least the last three millennia. 1 Abbott, B. W., Bishop, K., Zarnetske, J. P., Minaudo, C., Chapin, F. S., Krause, S., ... Plont, S. (2019). 12 Biancamaria, S., Durand, M., Andreadis, K. M., Bates, P. D., Boone, A., Mognard, N. M., ... Clark, E. A. (2011). 62 Srinivasan, V., Sanderson, M., Garcia, M., Konar, M., Blöschl, G., & Sivapalan, M. (2017). [Extracted from the article]

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is an indicator of sustainable land management as stated in the global indicator framework of the United Nations Sustainable Development Goals (SDG Indicator 15.3.1). Improved forecasting of future changes in SOM is needed to support the development of more sustainable land management under a changing climate. Current models fail to reproduce historical trends in SOM both within and during transition between ecosystems. More realistic spatio‐temporal SOM dynamics require inclusion of the recent paradigm shift from SOM recalcitrance as an 'intrinsic property' to SOM persistence as an 'ecosystem interaction'. We present a soil profile, or pedon‐explicit, ecosystem‐scale framework for data and models of SOM distribution and dynamics which can better represent land use transitions. Ecosystem‐scale drivers are integrated with pedon‐scale processes in two zones of influence. In the upper vegetation zone, SOM is affected primarily by plant inputs (above‐ and belowground), climate, microbial activity and physical aggregation and is prone to destabilization. In the lower mineral matrix zone, SOM inputs from the vegetation zone are controlled primarily by mineral phase and chemical interactions, resulting in more favourable conditions for SOM persistence. Vegetation zone boundary conditions vary spatially at landscape scales (vegetation cover) and temporally at decadal scales (climate). Mineral matrix zone boundary conditions vary spatially at landscape scales (geology, topography) but change only slowly. The thicknesses of the two zones and their transport connectivity are dynamic and affected by plant cover, land use practices, climate and feedbacks from current SOM stock in each layer. Using this framework, we identify several areas where greater knowledge is needed to advance the emerging paradigm of SOM dynamics—improved representation of plant‐derived carbon inputs, contributions of soil biota to SOM storage and effect of dynamic soil structure on SOM storage—and how this can be combined with robust and efficient soil monitoring. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review" which appeared in the previous issue.

As the editors of a scientific journal, we are privileged to have box seats to the peer review process. We watch as reviewers shine a new light on authors' work, identifying weakness in conception or expression, suggesting refinements, urging authors to clarify a point or better support an argument. The process is occasionally uncomfortable but it is almost always respectful and constructive and papers are uniformly better for this input. At the Journal of Advances in Modeling Earth Systems (JAMES) we are fortunate to have a pool of reviewers who volunteer their time, insight, knowledge, and intelligence to improve the work of their colleagues and peers. It is no small task: In 2018, we received 715 reviews by 494 individuals. On behalf of the entire editorial board and our authors we thank you all for your selfless contributions to strengthening our community. Key Point: On behalf of the entire editorial board and our authors we thank you all for your selfless contributions to strengthening our community [ABSTRACT FROM AUTHOR]

A severe drought hit the Greater Horn of Africa (GHA) in 2014, but it remains unclear whether this extreme event was attributable to anthropogenic climate change or part of longer‐term natural cycles. Precipitation patterns are known to be changing across the GHA, but trajectories in land surface variables are much less well known. We simulated the GHA land surface environment to assess the balance between natural cycles and human‐induced climate change. Using a new form of event attribution study where we focused on both climate variables and also directly simulated land surface variables, we combined publicly volunteered distributed computing with land surface simulations to quantify land surface responses. Uncertainty was quantified both for climate model and land surface model outputs. We identified two distinct "drought trajectories" in the GHA bimodal seasonality area during the March–May (Long Rains season) of 2014. Human‐induced climate change may have resulted in regions from Lake Nalubaale (Lake Victoria) to Northern Kenya receiving less precipitation in this season and having up to 20% higher probability of drought‐level evapotranspiration rates (increasing drought). In contrast, the simulated anthropogenic climate change signal for this season induced somewhat wetter conditions and up to 20% lower probability of drought‐level evapotranspiration in Eastern Ethiopia, Southern Somalia, and coastal Kenya (decreasing drought). Uncertainties in our modeling system varied by region and variable of focus, but broadly we found that land surface simulation uncertainty neither added significantly to climate model uncertainty nor significantly reduced it. Key Points: Precipitation patterns are known to be changing across the Horn of Africa, but trajectories in land surface variables are less well knownWe identify two distinct "drought trajectories" in the Horn of Africa: one of increasing drought and one of decreasing droughtChanges in water cycle impacts may best be assessed through attribution studies that include changes in land surface variables [ABSTRACT FROM AUTHOR]

The separation of evapotranspiration (ET) into its surface evaporation (E) and transpiration (T) components remains a challenge despite its importance for linking water and carbon cycles, for water management, and for attribution of hydrologic isotope fractionation. Regional and global estimates of surface evaporation often rely on estimates of ET (e.g., Penman‐Monteith) where E is deduced as a residual or as a fraction of potential evaporation. We propose a novel and direct method for estimating E from soil properties considering regional rainfall characteristics and accounting for internal drainage dynamics. A soil‐dependent evaporative characteristic length defines an active surface evaporative capacitor depth below which soil water is sheltered from capillary pull to the evaporating surface. A site‐specific evaporative capacitor is periodically recharged by rainfall and discharges at rates determined by interplay between internal drainage and surface evaporation. The surface evaporative capacitor concept was tested using field measurements and subsequently applied to generate a global map of climatic surface evaporation. Latitudinal comparisons with estimates from other global models (e.g., Penman‐Monteith method modified by Leuning et al., 2008, https://doi.org/10.1029/2007WR006562 [PML]; Moderate Resolution Imaging Spectroradiometer [MODIS]; and Global Land‐surface Evaporation: the Amsterdam Methodology [GLEAM]) show good agreement but also point to potential shortcomings of present estimates of surface evaporation. Interestingly, the ratio of surface evaporation (E) to potential evapotranspiration (ET0) is relatively constant across climates, biomes, and soil types with E/ET0 < 0.15 for 60% of all terrestrial surfaces, in agreement with recent studies. Key Points: A novel method for estimating surface evaporation from soil properties and accounting for internal drainage dynamics is presentedA soil‐dependent evaporative characteristic length defines an active surface evaporative capacitor (SEC) depthThe ratio of surface evaporation to potential evapotranspiration is relatively constant across climates, biomes, and soil types [ABSTRACT FROM AUTHOR]

This study compares four model systems in three configurations (LSM, LSM 1 GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally distributed observational monitoring. [ABSTRACT FROM AUTHOR]

It is hypothesized that more accurate prediction and warning of natural hazards, such as of the impacts of severe weather mediated through various components of the environment, require a more integrated Earth System approach to forecasting. This hypothesis can be explored using regional coupled prediction systems, in which the known interactions and feedbacks between different physical and biogeochemical components of the environment across sky, sea and land can be simulated. Such systems are becoming increasingly common research tools. This paper describes the development of the UKC2 regional coupled research system, which has been delivered under the UK Environmental Prediction Prototype project. This provides the first implementation of an atmosphere--land--ocean--wave modelling system focussed on the United Kingdom and surrounding seas at km-scale resolution. The UKC2 coupled system incorporates models of the atmosphere (Met Office Unified Model), land surface with river routing (JULES), shelf-sea ocean (NEMO) and ocean waves (WAVEWATCH III). These components are coupled, via OASIS3-MCT libraries, at unprecedentedly high resolution across the UK within a north-western European regional domain. A research framework has been established to explore the representation of feedback processes in coupled and uncoupled modes, providing a new research tool for UK environmental science. This paper documents the technical design and implementation of UKC2, along with the associated evaluation framework. An analysis of new results comparing the output of the coupled UKC2 system with relevant forced control simulations for six contrasting case studies of 5-day duration is presented. Results demonstrate that performance can be achieved with the UKC2 system that is at least comparable to its component control simulations. For some cases, improvements in air temperature, sea surface temperature, wind speed, significant wave height and mean wave period highlight the potential benefits of coupling between environmental model components. Results also illustrate that the coupling itself is not sufficient to address all known model issues. Priorities for future development of the UK Environmental Prediction framework and component systems are discussed. [ABSTRACT FROM AUTHOR]

Hydro-meteorological extremes such as drought and heavy precipitation can have large impacts on society and the economy. With potentially increasing risks associated with such events due to climate change, properly assessing the associated impacts and uncertainties is critical for adequate adaptation. However, the application of riskbased approaches often requires large sets of extreme events, which are not commonly available. Here, we present such a large set of hydro-meteorological time series for recent past and future conditions for the United Kingdom based on weather@home 2, a modelling framework consisting of a global climate model (GCM) driven by observed or projected sea surface temperature (SST) and sea ice which is downscaled to 25 km over the European domain by a regional climate model (RCM). Sets of 100 time series are generated for each of (i) a historical baseline (1900-2006), (ii) five nearfuture scenarios (2020-2049) and (iii) five far-future scenarios (2070-2099). The five scenarios in each future time slice all follow the Representative Concentration Pathway 8.5 (RCP8.5) and sample the range of sea surface temperature and sea ice changes from CMIP5 (Coupled Model Intercomparison Project Phase 5) models. Validation of the historical baseline highlights good performance for temperature and potential evaporation, but substantial seasonal biases in mean precipitation, which are corrected using a linear approach. For extremes in low precipitation over a long accumulation period (> 3 months) and shorter-duration high precipitation (1-30 days), the time series generally represents past statistics well. Future projections show small precipitation increases in winter but large decreases in summer on average, leading to an overall drying, consistently with the most recent UK Climate Projections (UKCP09) but larger in magnitude than the latter. Both drought and high-precipitation events are projected to increase in frequency and intensity in most regions, highlighting the need for appropriate adaptation measures. Overall, the presented dataset is a useful tool for assessing the risk associated with drought and more generally with hydro-meteorological extremes in the UK. [ABSTRACT FROM AUTHOR]

Land surface energy and water fluxes play an important role in land-atmosphere interactions, especially for the climatic feedback effects driven by land-use/landcover change (LULCC). These have long been documented in model-based studies, but the performance of land surface models in representing LULCC-induced responses has not been investigated well. In this study, measurements from proximate paired (open versus forest) flux tower sites are used to represent observed deforestation-induced changes in surface fluxes, which are compared with simulations from the Community Land Model (CLM) and the Noah Multi- Parameterization (Noah-MP) land model. Point-scale simulations suggest the CLM can represent the observed diurnal and seasonal changes in net radiation (Rnet/ and ground heat flux (G), but difficulties remain in the energy partitioning between latent (LE) and sensible (H) heat flux. The CLM does not capture the observed decreased daytime LE, and overestimates the increased H during summer. These deficiencies are mainly associated with models' greater biases over forest land-cover types and the parameterization of soil evaporation. Global gridded simulations with the CLM show uncertainties in the estimation of LE and H at the grid level for regional and global simulations. Noah-MP exhibits a similar ability to simulate the surface flux changes, but with larger biases in H, G, and Rnet change during late winter and early spring, which are related to a deficiency in estimating albedo. Differences in meteorological conditions between paired sites is not a factor in these results. Attention needs to be devoted to improving the representation of surface heat flux processes in land models to increase confidence in LULCC simulations. [ABSTRACT FROM AUTHOR]

We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme - Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (approximately 2:8° × 2:8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms - the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm (Ankerst et al., 1999; Daszykowski et al., 2002) and the algorithm of (Sander et al., 2003) - to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures; however, differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant-available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global-scale simulations using a simple gridmean value. [ABSTRACT FROM AUTHOR]

The article offers information on the Global Energy and Water-cycle Experiment (GEWEX) symposium on the global land-surface evaporation and climate. The symposium dealt with three issues including the estimated range of global land-surface evaporation developed from hydrological models, land surface parameterization, and remote sensing. It notes that through the meeting, important number of mature data-products became clear and that a dataset can now be offered to the global modelling community.

Vegetation exerts large control on global biogeochemical cycles through the processes of photosynthesis and transpiration that exchange CO2 and water between the land and the atmosphere. Increasing atmospheric CO2 concentrations exert direct effects on vegetation through enhanced photosynthesis and reduced stomatal conductance, and indirect effects through changes in climatic variables that drive these processes. How these direct and indirect CO2 impacts interact with each other to affect plant productivity and water use has not been explicitly analysed and remains unclear, yet is important to fully understand the response of the global carbon cycle to future climate change. Here, we use a set of factorial modelling experiments to quantify the direct and indirect impacts of atmospheric CO2 and their interaction on yield and water use in bioenergy short rotation coppice poplar, in addition to quantifying the impact of other environmental drivers such as soil type. We use the JULES land-surface model forced with a ten-member ensemble of projected climate change for 2100 with atmospheric CO2 concentrations representative of the A1B emissions scenario. We show that the simulated response of plant productivity to future climate change was nonadditive in JULES, however this nonadditivity was not apparent for plant transpiration. The responses of both growth and transpiration under all experimental scenarios were highly variable between sites, highlighting the complexity of interactions between direct physiological CO2 effects and indirect climate effects. As a result, no general pattern explaining the response of bioenergy poplar water use and yield to future climate change could be discerned across sites. This study suggests attempts to infer future climate change impacts on the land biosphere from studies that force with either the direct or indirect CO2 effects in isolation from each other may lead to incorrect conclusions in terms of both the direction and magnitude of plant response to future climate change. [ABSTRACT FROM AUTHOR]

Land Surface Models (LSMs) are prospective starting points to develop a global hyperresolution model of the terrestrial water, energy and biogeochemical cycles. However, there are some fundamental limitations of LSMs related to how meaningfully hydrological fluxes and stores are represented. A diagnostic approach to model evaluation is taken here that exploits hydrological expert knowledge to detect LSM inadequacies through consideration of the major behavioural functions of a hydrological system: overall water balance, vertical water redistribution in the unsaturated zone, temporal water redistribution and spatial water redistribution over the catchment's groundwater and surface water systems. Three types of information are utilised to improve the model's hydrology: (a) observations, (b) information about expected response from re-gionalised data, and (c) information from an independent physics-based model. The study considers the JULES (Joint UK Land Environmental Simulator) LSM applied to a deep-groundwater chalk catchment in the UK. The diagnosed hydrological limitations and the proposed ways to address them are indicative of the challenges faced while transitioning to a global high resolution model of the water cycle. [ABSTRACT FROM AUTHOR]

The article discusses research that examined the challenges inherent in detecting anthropogenic changes in the water cycle. Topics covered include the available observing capability to capture expected changes in the global water cycle, the increasing water content of the atmosphere, and the strengthening of climatological precipitation minus evaporation patterns.

Byline: Hedwig Roderfeld (1), Eleanor Blyth (2), Rutger Dankers (3), Geir Huse (4), Dag Slagstad (5), Ingrid Ellingsen (5), Annett Wolf (6), Manfred A. Lange (1) Abstract: The EU project BALANCE (Global Change Vulnerabilities in the Barents region: Linking Arctic Natural Resources, Climate Change and Economies) aims to assess vulnerability to climate change in the Barents Sea Region. As a prerequisite the potential impact of climate change on selected ecosystems of the study area has to be quantified, which is the subject of the present paper. A set of ecosystem models was run to generate baseline and future scenarios for 1990, 2020, 2050 and 2080. The models are based on data from the Regional Climate Model (REMO), driven by a GCM which in turn is forced by the IPCC-B2 scenario. The climate change is documented by means of the Koppen climate classification. Since the multitude of models requires the effect of climate change on individual terrestrial and marine systems to be integrated, the paper concentrates on a standardised visualisation of potential impacts by use of a Geographical Information System for the timeslices 2050 and 2080. The resulting maps show that both terrestrial and marine ecosystems of the Barents region will undergo significant changes until both 2050 and 2080. Author Affiliation: (1) Institute for Geophysics, University of Munster, Corrensstrasse 24, 48149, Munster, Germany (2) Centre for Ecology and Hydrology, Wallingford, OX10 8BB, UK (3) Institute for Environment and Sustainability, JRC European Commission, 21020, Ispra, (VA), Italy (4) Institute of Marine Research, P.O. Box 1870, Nordnes, 5817, Bergen, Norway (5) SINTEF Fisheries and Aquaculture, 465, Trondheim, Norway (6) Forest Ecology, Universitatstrasse 16, ETH-Zentrum, CHN, G 77, 8092, Zurich, Switzerland Article History: Registration Date: 05/10/2007 Received Date: 06/07/2006 Accepted Date: 03/10/2007 Online Date: 29/01/2008

Byline: Annett Wolf (1,2,5), Eleanor Blyth (3), Richard Harding (3), Daniela Jacob (4), Elke Keup-Thiel (4), Holger Goettel (4), Terry Callaghan (5,6) Abstract: We tested the sensitivity of a dynamic ecosystem model (LPJ-GUESS) to the representation of soil moisture and soil temperature and to uncertainties in the prediction of precipitation and air temperature. We linked the ecosystem model with an advanced hydrological model (JULES) and used its soil moisture and soil temperature as input into the ecosystem model. We analysed these sensitivities along a latitudinal gradient in northern Russia. Differences in soil temperature and soil moisture had only little influence on the vegetation carbon fluxes, whereas the soil carbon fluxes were very sensitive to the JULES soil estimations. The sensitivity changed with latitude, showing stronger influence in the more northern grid cell. The sensitivity of modelled responses of both soil carbon fluxes and vegetation carbon fluxes to uncertainties in soil temperature were high, as both soil and vegetation carbon fluxes were strongly impacted. In contrast, uncertainties in the estimation of the amount of precipitation had little influence on the soil or vegetation carbon fluxes. The high sensitivity of soil respiration to soil temperature and moisture suggests that we should strive for a better understanding and representation of soil processes in ecosystem models to improve the reliability of predictions of future ecosystem changes. Author Affiliation: (1) Department of Physical Geography and Ecosystem Analyses, Lund University, Lund, Sweden (2) Department of Environmental Science, Universitatsstr. 22, CH-8092, Zurich, Switzerland (3) Centre for Ecology and Hydrology, Wallingford, UK (4) Max-Planck-Institut fur Meteorologie (MPI-M), Hamburg, Germany (5) Abisko Scientific Research Station, Abisko, Sweden (6) Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK Article History: Registration Date: 04/10/2007 Received Date: 06/07/2006 Accepted Date: 03/10/2007 Online Date: 27/11/2007

Potential evaporation (ETP) is a basic input for many hydrological and agronomic models, as well as a key variable in most actual evaporation estimations. It has been approached through several diffusive and energy balance methods, out of which the Penman-Monteith equation is recommended as the standard one. In order to deal with the diffusive approach, ETP must be estimated at a sub-diurnal frequency, as currently done in land surface models (LSMs). This study presents an improved method, developed in the ORCHIDEE LSM, which consists of estimating ETP through an unstressed surface-energy balance (USEB method). The results confirm the quality of the estimation which is currently implemented in the model (Milly, 1992). The ETP underlying the reference evaporation proposed by the Food and Agriculture Organization, FAO, (computed at a daily time step) has also been analysed and compared. First, a comparison for a reference period under current climate conditions shows that USEB and FAO's ETP estimations differ, especially in arid areas. However, they produce similar values when the FAO's assumption of neutral stability conditions is relaxed, by replacing FAO's aerodynamic resistance by that of the model's. Furthermore, if the vapour pressure deficit (VPD) estimated for the FAO's equation, is substituted by ORCHIDEE's VPD or its humidity gradient, the agreement between the daily mean estimates of ETP is further improved. In a second step, ETP's sensitivity to climate change is assessed by comparing trends in these formulations for the 21st century. It is found that the USEB method shows a higher sensitivity than the FAO's. Both VPD and the model's humidity gradient, as well as the aerodynamic resistance have been identified as key parameters in governing ETP trends. Finally, the sensitivity study is extended to two empirical approximations based on net radiation and mass transfer (Priestley-Taylor and Rohwer, respectively). The sensitivity of these ETP estimates is compared to the one provided by USEB to test if simplified equations are able to reproduce the impact of climate change on ETP. [ABSTRACT FROM AUTHOR]

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Evaporation is made up of three components: interception, soil surface evaporation and transpiration. They each have different stores of water and different characteristic timescales. It would be useful to be able to identify the relative proportions of these components at large, even global, scales for land surface model validation. The possibility of separating out the components of observed evaporation at large scales is reviewed with reference to methods used at local scales, landscape scales and scales used in remote sensing. Many of the traditional methods used require intensive measurement programmes and are unlikely to be practical at large scales. New techniques using time-series analysis may be used to deliver a global three-component evaporation product. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Water-related impacts are among the most important consequences of increasing greenhouse gas concentrations. Changes in the global water cycle will also impact the carbon and nutrient cycles and vegetation patterns. There is already some evidence of increasing severity of floods and droughts and increasing water scarcity linked to increasing greenhouse gases. So far, however, the most important impacts on water resources are the direct interventions by humans, such as dams, water extractions, and river channel modifications. The Water and Global Change (WATCH) project is a major international initiative to bring together climate and water scientists to better understand the current and future water cycle. This paper summarizes the underlying motivation for the WATCH project and the major results from a series of papers published or soon to be published in the Journal of Hydrometeorology WATCH special collection. At its core is the Water Model Intercomparison Project (WaterMIP), which brings together a wide range of global hydrological and land surface models run with consistent driving data. It is clear that we still have considerable uncertainties in the future climate drivers and in how the river systems will respond to these changes. There is a grand challenge to the hydrological and climate communities to both reduce these uncertainties and communicate them to a wider society. [ABSTRACT FROM AUTHOR]

Recent developments to the Local-scale Urban Meteorological Parameterization Scheme (LUMPS), a simple model able to simulate the urban energy balance, are presented. The major development is the coupling of LUMPS to the Net All-Wave Radiation Parameterization (NARP). Other enhancements include that the model now accounts for the changing availability of water at the surface, seasonal variations of active vegetation, and the anthropogenic heat flux, while maintaining the need for only commonly available meteorological observations and basic surface characteristics. The incoming component of the longwave radiation ( L↓↓) in NARP is improved through a simple relation derived using cloud cover observations from a ceilometer collected in central London, England. The new L↓↓ formulation is evaluated with two independent multiyear datasets (ŁŁóódźź, Poland, and Baltimore, Maryland) and compared with alternatives that include the original NARP and a simpler one using the National Climatic Data Center cloud observation database as input. The performance for the surface energy balance fluxes is assessed using a 2-yr dataset (ŁŁóódźź). Results have an overall RMSE < 34 W m−−2 for all surface energy balance fluxes over the 2-yr period when using L↓↓ as forcing, and RMSE < 43 W m−−2 for all seasons in 2002 with all other options implemented to model L↓↓. [ABSTRACT FROM AUTHOR]

Surface energy flux measurements from a sample of 10 flux network (FLUXNET) sites selected to represent a range of climate conditions and biome types were used to assess the performance of the Hadley Centre land surface model (Joint U.K. Land Environment Simulator; JULES). Because FLUXNET data are prone systematically to undermeasure surface fluxes, the model was evaluated by its ability to partition incoming radiant energy into evaporation and how such partition varies with atmospheric evaporative demand at annual, seasonal, weekly, and diurnal time scales. The model parameters from the GCM configuration were used. The overall performance was good, although weaknesses in model performance were identified that are associated with the specification of the leaf area index and plant rooting depth, and the representation of soil freezing. [ABSTRACT FROM AUTHOR]

The newly developed WRF-Hydro model is a fully coupled atmospheric and hydrological processes model suitable for studying the intertwined atmospheric hydrological processes. This study utilizes the WRF-Hydro system on the Three-River source region. The Nash-Sutcliffe efficiency for the runoff simulation is 0.55 compared against the observed daily discharge amount of three stations. The coupled WRF-Hydro simulations are better than WRF in terms of six ground meteorological elements and turbulent heat flux, compared to the data from 14 meteorological stations located in the plateau residential area and two flux stations located around the lake. Although WRF-Hydro overestimates soil moisture, higher anomaly correlation coefficient scores (0.955 versus 0.941) were achieved. The time series of the basin average demonstrates that the hydrological module of WRF-hydro functions during the unfrozen period. The rainfall intensity and frequency simulated by WRF-Hydro are closer to global precipitation mission (GPM) data, attributed to higher convective available potential energy (CAPE) simulated by WRF-Hydro. The results emphasized the necessity of a fully coupled atmospheric-hydrological model when investigating land-atmosphere interactions on a complex topography and hydrology region. [ABSTRACT FROM AUTHOR]

Climate change is expected to alter the distribution of habitats and thus the distribution of species connected with these habitats in the terrestrial Barents Sea region. It was hypothesised that wild species connected with the tundra and open-land biome may be particularly at risk as forest area expands. Fourteen species of birds were identified as useful indicators for the biodiversity dependent upon this biome. By bringing together species distribution information with the LPJ-GUESS vegetation model, and with estimates of future wild and domestic reindeer density, potential impacts on these species between the present time and 2080 were assessed. Over this period there was a net loss of open land within the current breeding range of most bird species. Grazing reindeer were modelled as increasing the amount of open land retained for nine of the tundra bird species. [ABSTRACT FROM AUTHOR]