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Medium- to long-range precipitation forecasts are a crucial component in mitigating the impacts of fluvial flood events. Although precipitation is difficult to predict at these lead times, the forecast skill of atmospheric circulation tends to be greater. The study explores using weather patterns (WPs) as a preliminary step in producing forecasts of upper-tail precipitation threshold exceedance probabilities for the UK. The WPs are predefined, discrete states representing daily mean sea-level pressure (MSLP) over a European--North Atlantic domain. The WPs most likely to be associated with flooding are highlighted by calculating upper-tail exceedance probabilities derived from the conditional distributions of regional precipitation given each WP. WPs associated with higher probabilities of extreme precipitation are shown to have occurred during two well-known flood events: the 2014 Somerset Levels floods in southwest England; and Storm Desmond over the northern UK in December 2015. To illustrate the potential of this WP-based prediction framework, a forecast guidance tool called Fluvial Decider is introduced. It is intended for use by hydro-meteorologists in the England and Wales Flood Forecasting Centre (FFC). Forecasts of the MSLP from ensemble prediction systems (EPSs) are assigned to the closest-matching WP, providing daily probabilistic forecasts of WPs out to the chosen lead time. Combining these probabilities with observed precipitation threshold exceedance probabilities provides a parsimonious tool for highlighting potential periods with increased risk of flooding. Model forecasts using the European Centre forMedium-range Weather Forecasts (ECMWF) EPS highlighted both flood events as being at a higher than average risk of heavy extreme precipitation at lead times of over five days. [ABSTRACT FROM AUTHOR]

Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 d), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal timescales. Mean sea-level pressure forecasts from the European Centre for Medium-Range Weather Forecasts ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from the ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that the Markov model is the least skilful, while the dynamical WP model and direct precipitation forecasts have similar accuracy independent of lead time and season. However, drought forecasts are more reliable for the dynamical WP model. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8 and drought event hit and false alarm rates of 70 % and 30 %, respectively. [ABSTRACT FROM AUTHOR]

This study utilizes cluster analysis to produce sets of weather patterns for the Indian subcontinent. These patterns have been developed with future applications in mind; specifically relating to the occurrence of high‐impact weather and meteorologically induced hazards such as landslides. The weather patterns are also suited for use within probabilistic medium‐ to long‐range weather pattern forecasting tools driven by ensemble prediction systems. A total of 192 sets of weather patterns have been generated by varying the parameter which is clustered, the spatial domain and the number of weather patterns. Non‐hierarchical k‐means clustering was applied to daily 1200 UTC ERA‐Interim reanalysis data between 1979 and 2016 using pressure at mean sea level (PMSL) and u‐ and v‐component winds at 10‐m, 925‐hPa and 850‐hPa. The resultant weather pattern sets (clusters) were analysed for their ability to represent the main climatic precipitation patterns over India using the explained variation score. Weather patterns generated using 850‐hPa winds are among the most representative, with 30 patterns being enough to represent variability within different phases of the Indian climate. For example, several weather pattern variants are evident within the active monsoon, break monsoon and retreating monsoon. There are also several variants of weather patterns susceptible to western disturbances. These weather pattern variants are useful when it comes to identifying periods most susceptible to high‐impact weather within a large‐scale regime, such as identifying the most flood prone periods within the active monsoon. They hence have potentially many forecasting applications. [ABSTRACT FROM AUTHOR]

Dynamical model skill in forecasting extratropical precipitation is limited beyond the medium-range (around 15 days), but such models are often more skilful at predicting atmospheric variables. We explore the potential benefits of using weather pattern (WP) predictions as an intermediary step in forecasting UK precipitation and meteorological drought on sub-seasonal time scales. MSLP forecasts from the ECMWF ensemble prediction system (ECMWF-EPS) are post-processed into probabilistic WP predictions. Then we derive precipitation estimates and dichotomous drought event probabilities by sampling from the conditional distributions of precipitation given the WPs. We compare this model to the direct precipitation and drought forecasts from ECMWF-EPS and to a baseline Markov chain WP method. A perfect-prognosis model is also tested to illustrate the potential of WPs in forecasting. Using a range of skill diagnostics, we find that for 31- and 46-day lead-times, dynamical, and to a lesser extent Markov, model forecasts using WPs can achieve higher skill scores that the non-WP method, particularly for precipitation. Forecast skill scores are generally modest (rarely above 0.4), although those for the perfect-prognosis model highlight the potential predictability of precipitation and drought using WPs, with certain situations yielding skill scores of almost 0.8, and drought event hit and false alarm rates of 70% and 30%, respectively. [ABSTRACT FROM AUTHOR]

A medium‐ to long‐range forecast highlighting periods with an increased likelihood of coastal flooding is useful to United Kingdom governments and response agencies when considering proactive, large‐scale and multiregional responses. For this purpose, a new operational forecasting tool called Coastal Decider was developed for use by the Flood Forecasting Centre (FFC). Coastal Decider is used for internal preparedness within the FFC and provides the likelihood of coastal flooding around the UK, which is associated with the occurrence of specific predefined weather patterns in combination with high astronomical tides. Forecasts are available for medium‐range and monthly time‐scales using output from several ensemble prediction systems, whereby ensemble members are objectively assigned to the closest matching weather pattern definition. Ensemble members already clustered by weather pattern are then grouped to provide objective probabilities of a broader cluster of coastal‐risk weather patterns occurring. The coastal‐risk weather patterns were objectively derived for 21 coastal sites by relating daily historical weather pattern classifications to a wave hindcast, using a Met Office configuration of WAVEWATCH III, as well as observed skew surges, derived using tide‐gauge data from the British Oceanographic Data Centre (BODC). Finally, metrics based on the pressure‐anomaly difference between ensemble members and their assigned weather patterns, alongside forecasts of daily maximum 10 m wind speed are used to estimate the magnitude of coastal flooding. A set of 30 European weather patterns was objectively related to coastal flooding (caused by large waves and storm surges) at 21 sites around the United Kingdom. For example, weather pattern 14 (shown here) was a coastal‐risk weather pattern at Lowestoft on the east coast of England. These results were used to create a medium‐ to long‐range probabilistic weather‐pattern forecasting tool designed for highlighting forecast periods with a higher likelihood than normal of coastal flooding. [ABSTRACT FROM AUTHOR]

Objectives: In this study a prototype of a new health forecasting alert system is developed, which is aligned to the approach used in the Met Office’s (MO) National Severe Weather Warning Service (NSWWS). This is in order to improve information available to responders in the health and social care system by linking temperatures more directly to risks of mortality, and developing a system more coherent with other weather alerts. The prototype is compared to the current system in the Cold Weather and Heatwave plans via a case-study approach to verify its potential advantages and shortcomings. Method: The prototype health forecasting alert system introduces an “impact vs likelihood matrix” for the health impacts of hot and cold temperatures which is similar to those used operationally for other weather hazards as part of the NSWWS. The impact axis of this matrix is based on existing epidemiological evidence, which shows an increasing relative risk of death at extremes of outdoor temperature beyond a threshold which can be identified epidemiologically. The likelihood axis is based on a probability measure associated with the temperature forecast. The new method is tested for two case studies (one during summer 2013, one during winter 2013), and compared to the performance of the current alert system. Conclusions: The prototype shows some clear improvements over the current alert system. It allows for a much greater degree of flexibility, provides more detailed regional information about the health risks associated with periods of extreme temperatures, and is more coherent with other weather alerts which may make it easier for front line responders to use. It will require validation and engagement with stakeholders before it can be considered for use. [ABSTRACT FROM AUTHOR]

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended. [ABSTRACT FROM AUTHOR]

In order to monitor snow-cover dynamics in the Tana River Basin in Northern Fennoscandia, SPOT VEGETATION (VGT) images of the snowmelt seasons of 1998 and 1999 were used to identify snow-covered areas, employing an algorithm that was originally developed for data from the Moderate Resolution Imaging Spectroradiometer (MODIS). This algorithm is based on the Normalized Difference Snow Index (NDSI), which usually is calculated from the green and mid-infrared bands. In the absence of a green band, the applicability of this algorithm to VGT data from the red and mid-infrared bands was tested by comparing NDSI values with a corresponding Landsat Thematic Mapper (TM) image. The best agreement was found with slightly lower threshold values for the NDSI. Comparison of the snow-cover estimates also allowed testing of the performance of the NDSI-based algorithm in partially snow-free conditions. By applying the algorithm to ten-day syntheses of VGT images, the moment of snow disappearance could be registered for each 1×1 km pixel in the study area. The results were largely consistent with observations at meteorological stations in the area, confirming the effectiveness of VGT images and the algorithm employed in monitoring snow-cover depletion patterns. [ABSTRACT FROM AUTHOR]

The Long-Rains wet season of March–May (MAM) over Kenya in 2018 was one of the wettest on record. This paper examines the nature, causes, impacts, and predictability of the rainfall events, and considers the implications for flood risk management. The exceptionally high monthly rainfall totals in March and April resulted from several multi-day heavy rainfall episodes, rather than from distinct extreme daily events. Three intra-seasonal rainfall events in particular resulted in extensive flooding with the loss of lives and livelihoods, a significant displacement of people, major disruption to essential services, and damage to infrastructure. The rainfall events appear to be associated with the combined effects of active Madden–Julian Oscillation (MJO) events in MJO phases 2–4, and at shorter timescales, tropical cyclone events over the southwest Indian Ocean. These combine to drive an anomalous westerly low-level circulation over Kenya and the surrounding region, which likely leads to moisture convergence and enhanced convection. We assessed how predictable such events over a range of forecast lead times. Long-lead seasonal forecast products for MAM 2018 showed little indication of an enhanced likelihood of heavy rain over most of Kenya, which is consistent with the low predictability of MAM Long-Rains at seasonal lead times. At shorter lead times of a few weeks, the seasonal and extended-range forecasts provided a clear signal of extreme rainfall, which is likely associated with skill in MJO prediction. Short lead weather forecasts from multiple models also highlighted enhanced risk. The flood response actions during the MAM 2018 events are reviewed. Implications of our results for forecasting and flood preparedness systems include: (i) Potential exists for the integration of sub-seasonal and short-term weather prediction to support flood risk management and preparedness action in Kenya, notwithstanding the particular challenge of forecasting at small scales. (ii) We suggest that forecasting agencies provide greater clarity on the difference in potentially useful forecast lead times between the two wet seasons in Kenya and East Africa. For the MAM Long-Rains, the utility of sub-seasonal to short-term forecasts should be emphasized; while at seasonal timescales, skill is currently low, and there is the challenge of exploiting new research identifying the primary drivers of variability. In contrast, greater seasonal predictability of the Short-Rains in the October–December season means that greater potential exists for early warning and preparedness over longer lead times. (iii) There is a need for well-developed and functional forecast-based action systems for heavy rain and flood risk management in Kenya, especially with the relatively short windows for anticipatory action during MAM. [ABSTRACT FROM AUTHOR]

Abstract This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the ensemble mean (EM). The performance gain offered by MMC suggests that future multi-model applications consider reporting MMCs, alongside the EM and intermodal range, to provide end-users of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained. Highlights • We present the first use of machine learning-based model combination applied to a global hydrological model ensemble. • The multi-model combination (MMC) performs in most cases better than any individual input model and the ensemble mean. • MMC is not always able to out-perform model combination based on multiple linear regression. • The physical interpretation of the MMC solutions is limited by the complexity of their non-linear weighting schemes. [ABSTRACT FROM AUTHOR]

Future climate change and increasing atmospheric CO2 are expected to cause major changes in vegetation structure and function over large fractions of the global land surface. Seven global vegetation models are used to analyze possible responses to future climate simulated by a range of general circulation models run under all four representative concentration pathway scenarios of changing concentrations of greenhouse gases. All 110 simulations predict an increase in global vegetation carbon to 2100, but with substantial variation between vegetation models. For example, at 4 °C of global land surface warming (510-758 ppm of CO2), vegetation carbon increases by 52-477 Pg C (224 Pg C mean), mainly due to CO2 fertilization of photosynthesis. Simulations agree on large regional increases across much of the boreal forest, western Amazonia, central Africa, western China, and southeast Asia, with reductions across southwestern North America, central South America, southern Mediterranean areas, southwestern Africa, and southwestern Australia. Four vegetation models display discontinuities across 4 °C of warming, indicating global thresholds in the balance of positive and negative influences on productivity and biomass. In contrast to previous global vegetation model studies, we emphasize the importance of uncertainties in projected changes in carbon residence times. We find, when all seven models are considered for one representative concentration pathway × general circulation model combination, such uncertainties explain 30% more variation in modeled vegetation carbon change than responses of net primary productivity alone, increasing to 151% for non-HYBRID4 models. A change in research priorities away from production and toward structural dynamics and demographic processes is recommended. [ABSTRACT FROM AUTHOR]