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The UK Met Office state-of-the-art, deterministic, convection-permitting, coupled land-atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in-situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km2 gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw-Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field-scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS-UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS-UK soil moisture observations

Land surface models such as JULES (the Joint UK Land Environment Simulator) are usually run on a regular, rectilinear grid, resulting in gridded outputs for variables such as soil moisture and water fluxes. Here we investigate a method of clustering grid cells with similar characteristics together in JULES. Clustering grid cells has the potential to reduce computational expense as well as providing an alternative to tiling approaches for capturing sub-grid heterogeneity. In this study, we cluster grid cells exclusively in the land surface part of modelling, i.e., separate from river routing. We compare gridded and clustered soil moisture outputs from JULES with measurements from the UK Centre for Ecology and Hydrology (UKCEH) COSMOS-UK network and show that the clustering approach can model soil moisture well while reducing computational expense. However, soil moisture results are dependent on the characteristics used to create the clusters. We investigate the effect of using clusters on predicted river flows, and compare routed JULES outputs with NRFA gauge data in the catchment. We show that less expensive JULES clustered outputs give similar river flow results to standard gridded outputs when routed at the grid resolution, and are able to match observed river flow better than gridded outputs when routed at higher resolution.

In this report we document work aimed at improving the quality of the representation of land and rivers in multi-year, coupled atmosphere-land (UM-JULES) simulations over the British Isles. The approach taken was to use standalone (uncoupled) simulations of JULES to investigate the potential to improve the coupled system. The use of alternative soil ancillary information, generated by using a data assimilation framework and observations of soil moisture from the COSMOS-UK network to optimise the constants in a pedotransfer function, was found to result in improved simulations by JULES of river flow in a diverse sample of British rivers (as measured by standard statistics). The revised soil parameters tended to increase the variability of the simulated river on short timescales, and reduce variability on the annual timescale. In catchments with a large influence of slow baseflow the revised parameters tended to give poorer simulations, with too much variability on short timescales. A new representation of groundwater processes was implemented in JULES and applied, for the first time, across Great Britain. This was shown to allow an influence of groundwater on nearsurface hydrology and fluxes over large parts of the country. Modelled river flows were more realistic in many cases, though much of the improvement was due to differences in the representation of runoff generation rather than the introduction of groundwater. A more physically-complete parameterisation of river physics, using the local inertial equation, was applied in a range of catchments, again for the first time. Simulated river flows were improved in most cases. The final section of this report offers a perspective on how terrestrial hydrology and its impacts could be considered in the next generation of UK Climate Projections. The work reported here has significantly improved our capability in several areas of land surface modelling, to the extent that new developments could be tested in nationwide sim

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

The Hydro-JULES programme is supported under NERC National Capability and delivered by UKCEH in partnership with NCAS and BGS. The purpose of this document is to describe the Hydro-JULES modelling framework, which has been informed by several stakeholder consultations undertaken during the requirements-gathering phase of the programme.

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.

Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

We document the development of the first version of the United Kingdom Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models and new feedback mechanisms. These include: a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall the model performs well, with a stable pre‐industrial state, and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealised simulations show a high climate sensitivity relative to previous generations of models: equilibrium climate sensitivity (ECS) is 5.4 K, transient climate response (TCR) ranges from 2.68 K to 2.85 K, and transient climate response to cumulative emissions (TCRE) is 2.49 K/TtC to 2.66 K/TtC.

Photograph shows a lab assistant (?) labeling the bones for the bone boxes used by students studying human anatomy., 5 x 6 1/2 in.

Unidentified female physical therapy student standing behind a toddler walking up a wooden ramp., 21 x 26 cm

American Medical Missionary College class of 1899 stone was donated to Loma Linda University and arrived here on May 13, 1955 from Battle Creek. The ceremony of unveiling the stone was organized at Loma Linda on May 26, 1955. This photo was taken just east of the Anatomy Building., 13 x 18 cm

Portrait of Robert E. Cleveland. Cleveland served as academic vice president for Loma Linda University from 1964 to 1973. He has also served as provost for La Sierra College. Prior to his appointment at Loma Linda University, he was academic dean at Atlantic Union College in South Lancaster, Massachusetts. He graduated from Union College in Lincoln, Nebraska in 1948 with a bachelor's degree in history. Dr. Cleveland remained at that college for 10 years teaching both history and geography. He received his Doctor of Philosophy degree in modern European history from the University of Nebraska in 1957., 10 x 13 cm

Photograph of Robert Cleveland sitting at a desk reading an manuscript. Cleveland served as academic vice president for Loma Linda University from 1964 until 1973. He also served as provost for La Sierra College. Prior to his appointment at Loma Linda University, he was academic dean at Atlantic Union College in South Lancaster, Massachusetts. Dr. Cleveland graduated from Union College in Lincoln, Nebraska, in 1948 with a bachelor's degree in history. He remained at that college for 10 years teaching both history and geography. He received his Doctor of Philosophy degree in modern European history from the University of Nebraska in 1957., 21 x 25 cm

Photograph of Maynard V. Campbell seated. Campbell served as General Conference vice president from 1958 to 1968. Before his time serving in the General Conference, Campbell served as president of the Ontario, New York, Illinois, and Minnesota conferences. He also was on the board of trustees at Loma Linda University., 13 x 18 cm

Portrait of Maynard V. Campbell. Campbell served as General Conference vice president from 1958 to 1968. Before his time serving in the General Conference, Campbell served as president of the Ontario, New York, Illinois, and Minnesota conferences. He also was on the board of trustees at Loma Linda University., 12.5 x 18 cm

Photograph of Loma Linda University board of trustees member, Maynard V. Campbell., 13 x 18 cm

Linda Poole Barnes, the first student to graduate from Loma Linda University after it changed names from the College of Medical Evangelists in 1962., 8 x 10 in.

Photograph of Maynard V. Campbell seated. Campbell served as General Conference vice president from 1958 to 1968. Before his time serving in the General Conference, Campbell served as president of the Ontario, New York, Illinois, and Minnesota conferences. He also was on the board of trustees at Loma Linda University., 12.5 x 18 cm

Mary Brownsberger with sanitarium patient Mrs. Bruce (Glennys) Berndt., 10 x 11.5 cm

Alfred N. Brandon (left) was Library Director of the Vernier Radcliffe Memorial Library between 1953-1957., 13 x 18 cm

Godfrey T. Anderson and Jerry Wiley holding a newly publish book by Anderson., 20 x 20 cm

Portrait of Godfrey T. Anderson., 10 x 13 cm

Portrait of Ernest Charles Christensen and wife shaking the hand of Godfrey T. Anderson. Anderson was president of the College of Medical Evangelists (now Loma Linda University). Dr. Christensen graduated from the College of Medical Evangelists in 1943., 10 x 12.5 cm

Roy Allen Anderson conducts a Bible study with four students., 20 x 20.5 cm

Chaplain Horace Walsh conducts worship in the Loma Linda Sanitarium parlor., 13 x 18 cm

Front view of the church. It became the Campus Hill Church in 1966., 20.5 x 25 cm