Realism of rainfall in a very high-resolution regional climate model

The realistic representation of rainfall on the local scale in climate models remains a key challenge. Realism encompasses the full spatial and temporal structure of rainfall, and is a key indicator of model skill in representing the underlying processes. In particular, if rainfall is more realistic in a climate model, there is greater confidence in its projections of future change. In this study, the realism of rainfall in a very high-resolution (1.5 km) regional climate model (RCM) is compared to a coarser-resolution 12-km RCM. This is the first time a convection-permitting model has been run for an extended period (1989–2008) over a region of the United Kingdom, allowing the characteristics of rainfall to be evaluated in a climatological sense. In particular, the duration and spatial extent of hourly rainfall across the southern United Kingdom is examined, with a key focus on heavy rainfall. Rainfall in the 1.5-km RCM is found to be much more realistic than in the 12-km RCM. In the 12-km RCM, heavy rain events are not heavy enough, and tend to be too persistent and widespread. While the 1.5-km model does have a tendency for heavy rain to be too intense, it still gives a much better representation of its duration and spatial extent. Long-standing problems in climate models, such as the tendency for too much persistent light rain and errors in the diurnal cycle, are also considerably reduced in the 1.5-km RCM. Biases in the 12-km RCM appear to be linked to deficiencies in the representation of convection

The value of high-resolution Met Office regional climate models in the simulation of multi-hourly precipitation extremes

Open access articleExtreme value theory is used as a diagnostic for two high-resolution (12-km parameterized convection and 1.5-km explicit convection) Met Office regional climate model (RCM) simulations. On subdaily time scales, the 12-km simulation has weaker June–August (JJA) short-return-period return levels than the 1.5-km RCM, yet the 12-km RCM has overly large high return levels. Comparisons with observations indicate that the 1.5-km RCM is more successful than the 12-km RCM in representing (multi)hourly JJA very extreme events. As accumulation periods increase toward daily time scales, the erroneous 12-km precipitation extremes become more comparable with the observations and the 1.5-km RCM. The 12-km RCM fails to capture the observed low sensitivity of the growth rate to accumulation period changes, which is successfully captured by the 1.5-km RCM. Both simulations have comparable December–February (DJF) extremes, but the DJF extremes are generally weaker than in JJA at daily or shorter time scales. Case studies indicate that “gridpoint storms” are one of the causes of unrealistic very extreme events in the 12-km RCM. Caution is needed in interpreting the realism of 12-km RCM JJA extremes, including short-return-period events, which have return values closer to observations. There is clear evidence that the 1.5-km RCM has a higher degree of realism than the 12-km RCM in the simulation of JJA extremes.Natural Environment Research Council (NERC)UKMONewcastle Universit

Resilience of UK crop yields to compound climate change

Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate the impacts of future climate change on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is only partially explained by seasonal metrics of temperature and precipitation across key wheat growth stages (foundation, construction and production). We find climate impacts on wheat yields are strongest in years with compound weather extremes across multiple growth stages (e.g. frost and heavy rainfall). To assess how these conditions might evolve in the future, we analyse the latest 2.2 km UK Climate Projections (UKCP Local): on average, the foundation growth stage (broadly 1 October to 9 April) is likely to become warmer and wetter, while the construction (10 April to 10 June) and production (11 June to 26 July) stages are likely to become warmer and slightly drier. Statistical wheat yield projections, obtained by driving the regression model with UKCP Local simulations of precipitation and temperature for the UK's three main wheat-growing regions, indicate continued growth of crop yields in the coming decades. Significantly warmer projected winter night temperatures offset the negative impacts of increasing rainfall during the foundation stage, while warmer day temperatures and drier conditions are generally beneficial to yields in the production stage. This work suggests that on average, at the regional scale, climate change is likely to have more positive impacts on UK wheat yields than previously considered. Against this background of positive change, however, our work illustrates that wheat farming in the UK is likely to move outside of the climatic envelope that it has previously experienced, increasing the risk of unseen weather conditions such as intense local thunderstorms or prolonged droughts, which are beyond the scope of this paper

Soil erosion in a British watershed under climate change as predicted using convection-permitting regional climate projections

Climate change can lead to significant environmental and societal impacts; for example, through increases in the amount and intensity of rainfall with the associated possibility of flooding. Twenty-first-century climate change simulations for Great Britain reveal an increase in heavy precipitation that may lead to widespread soil loss by rising the likelihood of surface runoff. Here, hourly high-resolution rainfall projections from a 1.5 km (‘convection-permitting’) regional climate model are used to simulate the soil erosion response for two periods of the century (1996–2009 and a 13-year future period at ~2100) in the “Rother” catchment, West Sussex, England. Modeling soil erosion with EROSION 3D, we found a general increase in sediment production (off-site erosion) for the end of the century of about 43.2%, with a catchment-average increase from 0.176 to 0.252 t ha−1 y−1 and large differences between areas with diverse land use. These results highlight the effectiveness of using high-resolution rainfall projections to better account for spatial variability in the assessment of long-term soil erosion than other current methods

WRF simulations on the impacts and responses of extreme weather events: From the perspectives of climate change and urbanisation over UK cities

In the twenty-first century, extreme weather events leading to flooding and heat waves, have become one of the most severe challenges in urban areas, especially under the circumstances of local climate change and rapid urbanisation. In the future, cities are going to encounter more severe natural disaster risks and understanding how these could combine with modification of the urban environment (for example through adoption of green infrastructure) is critical for decisions relating to mitigation and adaptation to climate change. Green infrastructure is a subset of resilient infrastructure, which may mitigate the adverse effects caused by extreme weather and contribute to regulating urban climate. In addition, high-performing green spaces bring additional benefits for society in terms of health and wellbeing. The Weather Research and Forecasting (WRF) model is a numerical weather prediction system supporting both atmospheric research and operational forecasting. Within this modelling system, there is the possibility to modify parameters according to various urban areas within the WRF-Urban configuration. In this study, Newcastle upon Tyne (a UK city with the benefit of a lot of observational sensor data) is selected as an initial target city for identifying the optimal WRF configuration by varying the model resolution, domain size and nesting strategy. Future work will explore the influence of implementing green infrastructure in the context of climate change and urbanisation, then extending this analysis to London

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Convection in future winter storms over Northern Europe

International audiencePrecipitation within extratropical cyclones is very likely to increase towards the end of the century in a business-as-usual scenario. We investigate hourly precipitation changes in end-of-century winter storms with the first km-scale model ensemble covering northwest Europe and the Baltic region. This is an ensemble that explicitly represents convection (convection permitting models (CPMs)). Models agree that future winter storms will bring 10%–50% more precipitation, with the same level of light precipitation but more moderate and heavy precipitation, together with less frequent frozen precipitation. The warm sector precipitation rates will get closer (up to similar) to those in present-day autumn storms, along with higher convective available potential energy and convective inhibition, suggesting more convection embedded in storms. To the first order, mean hourly precipitation changes in winter storms are driven by temperature increase (with little relative humidity changes) and storm dynamical intensity (more uncertain), both captured by regional climate models (RCMs). The CPMs agree with this, and in addition, most CPMs show more increase in intense precipitation in the warm sector of storms compared to their parent RCM