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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 importa

There is an immediate need to develop accurate and reliable global hydrological forecasts in light of the future vulnerability to hydrological hazards and water scarcity under a changing climate. As a part of the World Meteorological Organization’s (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we investigated different approaches to blending multi-model simulations for developing holistic operational global forecasts. The ULYSSES (mULti-model hYdrological SeaSonal prEdictionS system) dataset, to be published as “Global seasonal forecasts and reforecasts of river discharge and related hydrological variables ensemble from four state-of-the-art land surface and hydrological models” is used in this study. The first step for improving these forecasts is to investigate ways to improve the model simulations, as global models are not calibrated for local conditions. The analysis was performed over 119 different catchments worldwide for the baseline period of 1981–2019 for three variables: evapotranspiration, surface soil moisture and streamflow. This study evaluated blending approaches with a performance metric based (weighted) averaging of the multi-model simulations, using the catchment’s Kling-Gupta Efficiency (KGE) for the variable to define the weight. Hydrological model simulations were also bias-corrected to improve the multi-model blending output. Weighted blending in conjunction with bias-correction provided the best improvement in performance for the catchments investigated. Applying modelled weights during blending original simulations improved performance over ungauged catchments. The results indicate that there is potential to successfully and easily implement the bias-corrected weighted blending approach to improve operational forecasts globally. This work can be used to improve water resources management and hydrological hazard mitigation, especially in data-sparse regions.

Environmental science is primarily concerned with assessing the impacts of changing environmental conditions on the state of the natural world, whether affected by natural variability or by the impact of human activity. The Natural Environment Research Council (NERC) has recently published its digital strategy1, the first of its kind for NERC, which sets out a vision for digitally enabled environmental science for the next decade. This is echoed in the Met Office’s Research and Innovation Strategy that includes the vision of transforming the weather and climate research and services through deploying transformative technologies such as Digital Twins2. This strategy places data and digital technologies at the heart of UK environmental science. One such set of technologies are digital twins. A digital twin is a virtual representation of an object or system (for example the natural environment) updated as the system changes using observations. Observations may come from a range of sources, some traditionally used in the environmental science community such as satellite remote sensing or sensors on ships or weather stations, or through the emergence of sensors on everything from fridges to cars to large-scale built infrastructure. A digital twin then uses simulations or data-based methods such as machine learning to generate a replica (‘twin’) of the system that can be used to understand the system itself. Environmental digital twins therefore have the potential to significantly improve our understanding of the natural environment. The emergence of increasingly large, diverse, observed data sources and the development of digital twin technologies combined provides an opportunity for the environmental science community to make a step-change in our understanding of the environment. But to realise the value of environmental digital twins they need to be developed following agreed standards to make sure the information can be trusted by the user, and so that data from twins

Environmental science is primarily concerned with assessing the impacts of changing environmental conditions on the state of the natural world. The UK Natural Environment Research Council (NERC) has recently published its digital strategy , the first of its kind for NERC, which sets out a vision for digitally enabled environmental science for the next decade. This is echoed in the UK Met Office’s Research and Innovation Strategy that includes the vision of transforming the weather and climate research and services through deploying transformative technologies such as Digital Twins . This strategy places data and digital technologies at the heart of UK environmental science. One such set of technologies are digital twins. Environmental digital twins have the potential to significantly improve our understanding of the natural environment. The emergence of increasingly large, diverse, observed data sources and the development of digital twin technologies combined provides an opportunity for the environmental science community to make a step-change in our understanding of the environment. But to realise the value of environmental digital twins they need to be developed following agreed standards where interoperability is required to make sure the information can be trusted by the user, and so that data from twins can be shared. To enable this, an information management framework (IMF) is needed that establishes the components for effective information management within and across the digital twin ecosystem. It must enable secure, resilient interoperability of data, and is a reference point to facilitate data use in line with security, legal, commercial, privacy and other relevant concerns. We present recommendations for the development of an IMF for Environmental digital twins (IMFe) including the application of concepts such as an asset commons and balanced approach to standards to facilitate minimum interoperability requirements between twins while iteratively impleme

Up to now, relatively little effort has been dedicated to the quantitative assessment of the differences in spatial patterns of model outputs. In this paper, we employed a variogram-based methodology to quantify the differences in the spatial patterns of root-zone soil moisture, net radiation, and latent and sensible heat fluxes simulated by three land surface models (SURFEX/ISBA, JULES and CHTESSEL) over three European geographic domains – namely, UK, France and Spain. The model output spatial patterns were quantified through two metrics derived from the variogram: i) the variogram sill, which quantifies the degree of spatial variability of the data; and ii) the variogram integral range, which represents the spatial length scale of the data. The higher seasonal variation of the spatial variability of sensible and latent heat fluxes over France and Spain, compared to the UK, is related to a more frequent occurrence of a soil-moisture-limited evapotranspiration regime during summer dry spells in the south of France and Spain. The small differences in spatial variability of net radiation between models indicate that the spatial patterns of net radiation are mostly driven by the climate forcing data set. However, the models exhibit larger differences in latent and sensible heat flux spatial variabilities, which are related to their differences in i) soil and vegetation ancillary datasets and ii) physical process representation. The highest discrepancies in spatial patterns between models are observed for soil moisture, which is mainly related to the type of soil hydraulic function implemented in the models. This work demonstrates the capability of the variogram to enhance our understanding of the spatiotemporal structure of the uncertainties in land surface model outputs. Therefore, we strongly encourage the implementation of the variogram metrics in model intercomparison exercises.

Soil moisture is an important component of the Earth system and plays a key role in land-atmosphere interactions. Remote sensing of soil moisture is of great scientific interest and the scientific community has made significant progress in soil moisture estimation using Earth observations. Currently, several satellite-based coarse spatial resolution soil moisture datasets have been produced and widely used for various applications in climate science, hydrology, ecosystem research and agriculture. Owing to the strong demand for soil moisture data with high spatial resolution for regional applications, much effort has recently been devoted to the generation of high spatial resolution soil moisture data from either high-resolution satellite observations or by downscaling existing coarse-resolution satellite-based soil moisture datasets. In addition, land surface models provide an alternative way to obtain consistent high-resolution soil moisture information when forced with high-resolution inputs. The aim of this study is to create and evaluate high-resolution soil moisture products derived from multiple sources including satellite observations and land surface model simulations. The JULES-CHESS simulated soil moisture and satellite-based soil moisture datasets including SMAP L3E, SMAP L4, SMOS L4, Sentinel 1, ASCAT, and Sentinel 1/SMAP combined products were first validated against observed soil moisture from COSMOS-UK, a network of in-situ cosmic-ray based sensors. Second, an approach based on triple collocation was applied to compare these satellite products in the absence of a known reference dataset. Third, a combined soil moisture product was generated to integrate the better-performing soil moisture estimates based on triple collocation error estimation and a least-squares merging scheme. From further evaluation, it is found that the merged soil moisture integrates the characteristics of model simulation and satellite observations and particularly improves the l

Objectives: Cytomegalovirus (CMV) is known to have a significant impact on immune recovery post-allogeneic haemopoietic stem cell transplant (HSCT). Adoptive therapy with donor-derived or third-party virus-specific T cells (VST) can restore CMV immunity leading to clinical benefit in prevention and treatment of post-HSCT infection. We developed a mass cytometry approach to study natural immune recovery post-HSCT and assess the mechanisms underlying the clinical benefits observed in recipients of VST. Methods: A mass cytometry panel of 38 antibodies was utilised for global immune assessment (72 canonical innate and adaptive immune subsets) in HSCT recipients undergoing natural post-HSCT recovery (n = 13) and HSCT recipients who received third-party donor-derived CMV-VST as salvage for unresponsive CMV reactivation (n = 8). Results: Mass cytometry identified distinct immune signatures associated with CMV characterised by a predominance of innate cells (monocytes and NK) seen early and an adaptive signature with activated CD8+ T cells seen later. All CMV-VST recipients had failed standard antiviral pharmacotherapy as a criterion for trial involvement; 5/8 had failed to develop the adaptive immune signature by study enrolment despite significant CMV antigen exposure. Of these, VST administration resulted in development of the adaptive signature in association with CMV control in three patients. Failure to respond to CMV-VST in one patient was associated with persistent absence of the adaptive immune signature. Conclusion: The clinical benefit of CMV-VST may be mediated by the recovery of an adaptive immune signature characterised by activated CD8+ T cells.

Human cytomegalovirus (HCMV) is the most frequent cause of opportunistic viral infection following transplantation. Viral factors of potential clinical importance include the selection of mutants resistant to antiviral drugs and the occurrence of infections involving multiple HCMV strains. These factors are typically addressed by analyzing relevant HCMV genes by PCR and Sanger sequencing, which involves independent assays of limited sensitivity. To assess the dynamics of viral populations with high sensitivity, we applied high-throughput sequencing coupled with HCMV-adapted target enrichment to samples collected longitudinally from 11 transplant recipients (solid organ, n = 9, and allogeneic hematopoietic stem cell, n = 2). Only the latter presented multiple-strain infections. Four cases presented resistance mutations (n = 6), two (A594V and L595S) at high (100%) and four (V715M, V781I, A809V, and T838A) at low (<25%) frequency. One allogeneic hematopoietic stem cell transplant recipient presented up to four resistance mutations, each at low frequency. The use of high-throughput sequencing to monitor mutations and strain composition in people at risk of HCMV disease is of potential value in helping clinicians implement the most appropriate therapy.

Introduction: A pandemic coronavirus, SARS-CoV-2, causes COVID-19, a potentially life-threatening respiratory disease. Patients with cancer may have compromised immunity due to their malignancy and/or treatment, and may be at elevated risk of severe COVID-19. Community transmission of COVID-19 could overwhelm health care services, compromising delivery of cancer care. This interim consensus guidance provides advice for clinicians managing patients with cancer during the pandemic. Main recommendations: During the COVID-19 pandemic:. In patients with cancer with fever and/or respiratory symptoms, consider causes in addition to COVID-19, including other infections and therapy-related pneumonitis. For suspected or confirmed COVID-19, discuss temporary cessation of cancer therapy with a relevant specialist. Provide information on COVID-19 for patients and carers. Adopt measures within cancer centres to reduce risk of nosocomial SARS-CoV-2 acquisition; support population-wide social distancing; reduce demand on acute services; ensure adequate staffing; and provide culturally safe care. Measures should be equitable, transparent and proportionate to the COVID-19 threat. Consider the risks and benefits of modifying cancer therapies due to COVID-19. Communicate treatment modifications, and review once health service capacity allows. Consider potential impacts of COVID-19 on the blood supply and availability of stem cell donors. Discuss and document goals of care, and involve palliative care services in contingency planning. Changes in management as a result of this statement: This interim consensus guidance provides a framework for clinicians managing patients with cancer during the COVID-19 pandemic. In view of the rapidly changing situation, clinicians must also monitor national, state, local and institutional policies, which will take precedence. Endorsed by: Australasian Leukaemia and Lymphoma Group; Australasian Lung Cancer Trials Group; Australian and New Zealand Childr

INTRODUCTION: A pandemic coronavirus, SARS-CoV-2, causes COVID-19, a potentially life-threatening respiratory disease. Patients with cancer may have compromised immunity due to their malignancy and/or treatment, and may be at elevated risk of severe COVID-19. Community transmission of COVID-19 could overwhelm health care services, compromising delivery of cancer care. This interim consensus guidance provides advice for clinicians managing patients with cancer during the pandemic. MAIN RECOMMENDATIONS: During the COVID-19 pandemic: In patients with cancer with fever and/or respiratory symptoms, consider causes in addition to COVID-19, including other infections and therapy-related pneumonitis. For suspected or confirmed COVID-19, discuss temporary cessation of cancer therapy with a relevant specialist. Provide information on COVID-19 for patients and carers. Adopt measures within cancer centres to reduce risk of nosocomial SARS-CoV-2 acquisition; support population-wide social distancing; reduce demand on acute services; ensure adequate staffing; and provide culturally safe care. Measures should be equitable, transparent and proportionate to the COVID-19 threat. Consider the risks and benefits of modifying cancer therapies due to COVID-19. Communicate treatment modifications, and review once health service capacity allows. Consider potential impacts of COVID-19 on the blood supply and availability of stem cell donors. Discuss and document goals of care, and involve palliative care services in contingency planning. CHANGES IN MANAGEMENT AS A RESULT OF THIS STATEMENT: This interim consensus guidance provides a framework for clinicians managing patients with cancer during the COVID-19 pandemic. In view of the rapidly changing situation, clinicians must also monitor national, state, local and institutional policies, which will take precedence. ENDORSED BY: Australasian Leukaemia and Lymphoma Group; Australasian Lung Cancer Trials Group; Australian and New Zealand Childre

The integrated land ecosystem-atmosphere processes study (iLEAPS) is an international research project focussing on the fundamental processes that link land-atmosphere exchange, climate, the water cycle, and tropospheric chemistry. The project, iLEAPS, was established 2004 within the International Geosphere-Biosphere Programme (IGBP). During its first decade, iLEAPS has proven to be a vital project, well equipped to build a community to address the challenges involved in understanding the complex Earth system: multidisciplinary, integrative approaches for both observations and modeling. The iLEAPS community has made major advances in process understanding, land-surface modeling, and observation techniques and networks. The modes of iLEAPS operation include elucidating specific iLEAPS scientific questions through networks of process studies, field campaigns, modeling, long-term integrated field studies, international interdisciplinary mega-campaigns, synthesis studies, databases, as well as conferences on specific scientific questions and synthesis meetings. Another essential component of iLEAPS is knowledge transfer and it also encourages community-and policy-related outreach activities associated with the regional integrative projects. As a result of its first decade of work, iLEAPS is now setting the agenda for its next phase (2014-2024) under the new international initiative, future Earth. Human influence has always been an important part of land-atmosphere science but in order to respond to the new challenges of global sustainability, closer ties with social science and economics groups will be necessary to produce realistic estimates of land use and anthropogenic emissions by analysing future population increase, migration patterns, food production allocation, land management practices, energy production, industrial development, and urbanization.

Current techniques to disentangle the evaporative fluxes from the continental surface into a contribution evaporated from soils and canopy, or transpired by plants, are under debate. Many isotope-based studies show that transpiration contributes generally more than 70% to the total evaporation, while other isotope-independent techniques lead to considerably smaller transpiration fractions. This paper provides a perspective on isotope-based versus non-isotope-based partitioning studies. Some partitioning results from isotope-based methods, hydrometric measurements, and modeling are presented for comparison. Moreover, the methodological aspects of the partitioning analysis are considered, including their limitations, and explanations of possible discrepancies between the methods are discussed. We suggest sources of systematic error that may lead to biases in the results, e.g., instruments inaccuracy, assumptions used in analyses, and calibration parameters. A number of comparison studies using isotope-based methods and hydrometric measurements in the same plants and climatic conditions are consistent within the errors; however, models tend to produce lower transpiration fractions. The relatively low transpiration fraction in current state-of-the-art land-surface models calls for a reassessment of the skill of the underlying model parameterizations. The scarcity of global evaporation data makes calibration and validation of global isotope-independent and isotope-based results difficult. However, isotope-enabled land-surface and global climate modeling studies allow for the evaluation of the parameterization of land-surface models by comparing the computed water isotopologue signals in the atmosphere with the available remote sensing and flux-based data sets. Future studies that allow for this evaluation could provide a better understanding of the hydrological cycle in vegetated regions.

Water Management, Civil Engineering and Geosciences

A parameterisation to incorporate the effects of frozen soil on modelled hydrology is described and implemented within a land surface model, the Joint UK Land Surface Environment Simulator. It is shown to generally improve the modelled flow of Siberian rivers compared to observations, specifically in seasons of freezing or thawing soil. Most noticeably, the revised model increases the snowmelt flow peak by 26–100% compared to the control model thereby better matching observed flows. The model physics resulting in the changes to river flow are discussed and attention is given to the effect of inaccuracies in snowfall driving data which can hinder the comparison of new model processes.

Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to

Wetlands are generally accepted as being the largest but least well quantified single source of methane (CH4). The extent of wetland or inundation is a key factor controlling methane emissions, both in nature and in the parameterisations used in large-scale land surface and climate models. Satellite-derived datasets of wetland extent are available on the global scale, but the resolution is rather coarse (>25 km). The purpose of the present study is to assess the capability of active microwave sensors to derive inundation dynamics for use in land surface and climate models of the boreal and tundra environments. The focus is on synthetic aperture radar (SAR) operating in C-band since, among microwave systems, it has comparably high spatial resolution and data availability, and long-term continuity is expected. C-band data from ENVISAT ASAR (Advanced SAR) operating in wide swath mode (150 m resolution) were investigated and an automated detection procedure for deriving open water fraction has been developed. More than 4000 samples (single acquisitions tiled onto 0.5° grid cells) have been analysed for July and August in 2007 and 2008 for a study region in Western Siberia. Simple classification algorithms were applied and found to be robust when the water surface was smooth. Modification of input parameters results in differences below 1 % open water fraction. The major issue to address was the frequent occurrence of waves due to wind and precipitation, which reduces the separability of the water class from other land cover classes. Statistical measures of the backscatter distribution were applied in order to retrieve suitable classification data. The Pearson correlation between each sample dataset and a location specific representation of the bimodal distribution was used. On average only 40 % of acquisitions allow a separation of the open water class. Although satellite data are available every 2–3 days over the Western Siberian study region, the irregular acquisition

Recent decades have seen large changes in the environment of high northern latitude land areas, including warming and changes in snow and vegetation cover, and these are the subject of research using increasingly sophisticated models. There is a need to evaluate the models against observational data, both to quantify the performance of the current models and to guide future developments. Although data from field campaigns are an important resource, Earth Observation (EO) products are increasingly being used as the only practicable means for evaluation at the large spatial scales over which models are routinely applied. The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. JULES includes parameterisations of cold season processes, including a multi-layer model of the snow pack and soil freezing and thawing processes. In this paper we compare simulations of the Northern Hemisphere using JULES to a range of EO products. The EO products monitor snow cover (e.g. from ESA DUE GlobSnow, MODIS), snow water equivalent (e.g. GlobSnow), surface state (frozen/unfrozen/melting, from ESA STSE ALANIS-Methane/DUE Permafrost) and surface temperature (e.g. AATSR, MODIS). Uncertainty in the EO products arises in part because these products themselves involve models of radiative transfer processes. By comparing several quantities from a range of products we develop a consistent picture of the performance of JULES over large areas, and get a qualitative estimate of EO uncertainty. We also investigate uncertainty in the outputs from JULES that arises from uncertainties in the parameterisations and in the input data (e.g. meteorology). We propose to incorporate EO products that describe snow and frozen ground into the routine benchmarking of JULES and other land surface models.

The Water and Global Change (WATCH) project evaluation of the terrestrial water cycle involves using land surface models and general hydrological models to assess hydrologically important variables including evaporation, soil moisture, and runoff. Such models require meteorological forcing data, and this paper describes the creation of the WATCH Forcing Data for 1958–2001 based on the 40-yr ECMWF Re-Analysis (ERA-40) and for 1901–57 based on reordered reanalysis data. It also discusses and analyses model-independent estimates of reference crop evaporation. Global average annual cumulative reference crop evaporation was selected as a widely adopted measure of potential evapotranspiration. It exhibits no significant trend from 1979 to 2001 although there are significant long-term increases in global average vapor pressure deficit and concurrent significant decreases in global average net radiation and wind speed. The near-constant global average of annual reference crop evaporation in the late twentieth century masks significant decreases in some regions (e.g., the Murray–Darling basin) with significant increases in others.

This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides a unique opportunity for the research community to contribute their research to improve both worldleading operational weather forecasting and climate change prediction systems. In addition JULES, and its forerunner MOSES, have been the basis for a number of very highprofile papers concerning the land-surface and climate over the last decade. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.