Use of very high resolution climate model data for hydrological modelling: estimation of potential evaporation

Climate model data are increasingly used to drive hydrological models, to assess the possible impacts of climate change on river flows. Hydrological models often require potential evaporation (PE) from vegetation, alongside precipitation, but PE is not usually output by climate models so has to be estimated from other meteorological variables. Here, the Penman-Monteith formula is applied to estimate PE using data from a 12 km Regional Climate Model (RCM) and a nested very high resolution (1.5 km) RCM covering southern Britain. PE estimates from RCM runs driven by reanalysis boundary conditions are compared to observation-based PE data, to assess performance. The comparison shows that both the 1.5 and 12 km RCMs reproduce observation-based PE well, on daily and monthly time-steps, and enables choices to be made about application of the formula using the available data. Data from Current and Future RCM runs driven by boundary conditions from a Global Climate Model are then used to investigate potential future changes in PE, and how certain factors affect those changes. In particular, the importance of including changes in canopy resistance is demonstrated. PE projections are also shown to vary to some extent according to how aerosols are modelled in the RCMs

Use of very high resolution climate model data for hydrological modelling in southern Britain

Previous work driving hydrological models directly with data from regional climate models (RCMs) used data on an approximately 25x25km grid, which generally required some form of further downscaling before use by hydrological models. Recently, higher resolution data have become available from a NERC Changing Water Cycle project, CONVEX. As part of that project the Met Office Hadley Centre has run a very high resolution (1.5km) RCM, nested in a 12km RCM driven by ERA-Interim boundary conditions (1989-2008). They have also run baseline and future climate scenarios, nesting the RCMs in a global climate model. The 12km RCM runs cover Europe, while the 1.5km RCM runs only cover southern Britain

Representation of model error in a convective-scale ensemble prediction system

In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office’s 24- member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model’s parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits “jumpiness” in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone

Understanding the national performance of flood forecasting models to guide incident management and investment

The preparation of routine flood guidance statements and formulation of incident management strategies requires national operating agencies to have a firm understanding of the performance of flood forecasting models. Studies of flood forecasting model performance are commonly evaluated on a groupedcatchment or local basis and can lack the analytical consistency required for integration into coherent national assessments. Here, the first nationally consistent analysis of flood forecasting model performance across England and Wales is presented. Application of the assessment framework, accounting for regional and model-type differences, yields a national overview of relative forecasting capability for models in current operational use. To achieve extensive site coverage, information from many existing local performance studies are pooled into a single structure for analysis under a national framework. The performance information spanning a variety of local models is also compared against the area-wide national G2G (Grid-to-Grid) distributed model. An integrated national assessment gives an evidence base of model performance useful for guiding strategic planning and investment in flood forecasting models. A concise single-page Performance Summary has been created for each site model that contains performance statistics, forecast hydrographs and catchment properties to aid operational use. A prototype web portal has been developed to make information on forecasting model performance more accessible and understandable for end-users

Methods of investigating forecast error sensitivity to ensemble size in a limited-area convection-permitting ensemble

Ensemble-based predictions are increasingly used as an aid to weather forecasting and to data assimilation, where the aim is to capture the range of possible outcomes consistent with the underlying uncertainties. Constraints on computing resources mean that ensembles have a relatively small size, which can lead to an incomplete range of possible outcomes, and to inherent sampling errors. This paper discusses how an existing ensemble can be relatively easily increased in size, it develops a range of standard and extended diagnostics to help determine whether a given ensemble is ‘large enough’ to be useful for forecasting and data assimilation purposes, and it applies the diagnostics to a convective-scale case study for illustration. Diagnostics include the effect of ensemble size on various aspects of rainfall forecasts, kinetic energy spectra, and (co)variance statistics in the spatial and spectral domains. The work here extends the Met Office’s 24 ensemble members to 93. It is found that the extra members do develop a significant degree of linear independence, they increase the ensemble spread (although with caveats to do with non-Gaussianity), they reduce sampling error in many statistical quantities (namely variances, correlations, and length-scales), and improve the effective spatial resolution of the ensemble. The extra members though do not improve the probabilistic rain rate forecasts. It is assumed that the 93-member ensemble approximates the error-free statistics, which is a practical assumption, but the data suggests that this number of members is ultimately not enough to justify this assumption, and therefore more ensembles are likely required for such convective-scale systems to further reduce sampling errors, especially for ensemble data assimilation purposes

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961–2012, by interpolating coarser resolution climate data and including the effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day−1 decade−1 in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day−1 decade−1 in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day−1 decade−1. The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day−1 decade−1 (0.038 ± 0.018 mm day−1 decade−1 when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short- and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring

Glycogen synthase kinase 3 (GSK-3) controls T-cell motility and interactions with antigen presenting cells.

OBJECTIVE: The threonine/serine kinase glycogen synthase kinase 3 (GSK-3) targets multiple substrates in T-cells, regulating the expression of Tbet and PD-1 on T-cells. However, it has been unclear whether GSK-3 can affect the motility of T-cells and their interactions with antigen presenting cells. RESULTS: Here, we show that GSK-3 controls T-cell motility and interactions with other cells. Inhibition of GSK-3, using structurally distinct inhibitors, reduced T-cell motility in terms of distance and displacement. While SB415286 reduced the number of cell-cell contacts, the dwell times of cells that established contacts with other cells did not differ for T-cells treated with SB415286. Further, the increase in cytolytic T-cell (CTL) function in killing tumor targets was not affected by the inhibition of motility. This data shows that the inhibition of GSK-3 has differential effects on T-cell motility and CTL function where the negative effects on cell-cell interactions is overridden by the increased cytolytic potential of CTLs

Glycogen synthase kinase 3 (GSK-3) controls T-cell motility and interactions with antigen presenting cells.

OBJECTIVE: The threonine/serine kinase glycogen synthase kinase 3 (GSK-3) targets multiple substrates in T-cells, regulating the expression of Tbet and PD-1 on T-cells. However, it has been unclear whether GSK-3 can affect the motility of T-cells and their interactions with antigen presenting cells. RESULTS: Here, we show that GSK-3 controls T-cell motility and interactions with other cells. Inhibition of GSK-3, using structurally distinct inhibitors, reduced T-cell motility in terms of distance and displacement. While SB415286 reduced the number of cell-cell contacts, the dwell times of cells that established contacts with other cells did not differ for T-cells treated with SB415286. Further, the increase in cytolytic T-cell (CTL) function in killing tumor targets was not affected by the inhibition of motility. This data shows that the inhibition of GSK-3 has differential effects on T-cell motility and CTL function where the negative effects on cell-cell interactions is overridden by the increased cytolytic potential of CTLs

Spatial downscaling of precipitation for hydrological modelling: assessing a simple method and its application under climate change in Britain

National or regional grid-based hydrological models are usually run at relatively fine spatial resolutions. But the meteorological data necessary to drive such models are often coarser resolution, so some form of spatial downscaling is generally required. A 1km hydrological model for Great Britain is used to test the performance of a simple method of downscaling precipitation based on 1km patterns of long-term mean annual rainfall (Standard Average Annual Rainfall; SAAR). For a range of coarser resolutions (5, 10, 25 and 50km), a 1km grid of multiplicative scaling factors is derived as the ratio of the 1km grid box SAAR divided by the mean SAAR of the coarser resolution grid box that contains it. A dataset of 1km daily observation-based precipitation is then degraded to the coarser resolutions, and application of SAAR scaling factors is compared to no downscaling and direct use of 1km data, for simulating river flows for a large set of catchments. SAAR-based downscaling provides a clear improvement over no downscaling. Using monthly rather than annual long-term mean rainfall patterns provides minimal further improvement. There are no strong relationships between performance and catchment properties, but performance using 50km precipitation without downscaling tends to be worse for smaller, steeper catchments and those with a more south-westerly aspect; these benefit more from SAAR-based downscaling. An assessment using high resolution convection-permitting model data shows relatively small changes in derived SAAR scaling factors between a baseline and far-future period, suggesting that use of historical scaling factors for future periods is reasonable. Applicability of this simple downscaling method for other parts of the world should be similarly assessed, for both historical and future periods. While use of annual patterns seems to be sufficient in Britain, areas where spatial rainfall patterns are more variable through the year may require use of sub-annual patterns

A long-term national-scale hydrological simulation of river flows across Great Britain

The Centre for Ecology and Hydrology’s national-scale hydrological model, Grid-to-Grid, can be used to estimate river flows and soil moisture across Great Britain. It is used operationally at the flood forecasting centre and there have been a number of studies on floods and climate change using this model, however to date, low flows and droughts have been comparatively neglected. The launch of a five-year NERC-funded interdisciplinary research programme “UK Droughts and Water Scarcity” is allowing us to address this. Our work on one of these projects, MaRIUS (Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity), uses the model to identify drought periods. The model is driven by a new long-term (1890–2012) precipitation dataset (CEH-GEAR) and estimates of potential evaporation. Model performance is assessed against observed river flows for both high and low flows. Gridded time series of monthly mean river flow and soil moisture from the model have been analysed to identify historic hydrological droughts across Great Britain using concepts such as severity and duration. We also investigate how drought occurrence and severity have changed over the last 100 years and identify regions that have been particularly susceptible to drought