Stakeholder perceptions of event attribution in the loss and damage debate

In 2013 the Warsaw International Mechanism (WIM) for loss and damage (L&D) associated with climate change impacts was established under the United Nations Framework Convention on Climate Change (UNFCCC). For scientists, L&D raises ques- tions around the extent that such impacts can be attributed to anthropogenic climate change, which may generate complex results and be controversial in the policy arena. This is particularly true in the case of probabilistic event attribution (PEA) science, a new and rapidly evolving field that assesses whether changes in the probabilities of extreme events are attributable to GHG emissions. If the potential applications of PEA are to be considered responsibly, dialogue between scientists and policy makers is fundamental. Two key questions are considered here through a literature review and key stakeholder interviews with representatives from the science and policy sectors underpinning L&D. These provided the opportunity for in-depth insights into stakeholders’ views on firstly, how much is known and understood about PEA by those associated with the L&D debate? Secondly, how might PEA inform L&D and wider climate policy? Results show debate within the climate science community, and limited understanding among other stakeholders, around the sense in which extreme events can be attributed to climate change. However, stake- holders do identify and discuss potential uses for PEA in the WIM and wider policy, but it remains difficult to explore precise applications given the ambiguity surrounding L&D. This implies a need for stakeholders to develop greater understandings of alternative conceptions of L&D and the role of science, and also identify how PEA can best be used to support policy, and address associated challenges.

Continental and global scale flood forecasting systems

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centres are increasingly using the meteorological output from these to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Currently, operational systems have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products, in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale, alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction.

Characterisation of convective regimes over the British Isles

Convection-permitting modelling has led to a step change in forecasting convective events. However, convection occurs within different regimes which exhibit different forecast behaviour. A convective adjustment timescale can be used to distinguish between these regimes and examine their associated predictability. The convective adjustment timescale is calculated from radiosonde ascents and found to be consistent with that derived from convection-permitting model forecasts. The model-derived convective adjustment timescale is then examined for three summers in the British Isles to determine characteristics of the convective regimes for this maritime region. Convection in the British Isles is predominantly in convective quasi-equilibrium with 85%of convection having a timescale less than or equal to three hours. This percentage varies spatially with more non-equilibriumevents occurring in the south and southwest. The convective adjustment timescale exhibits a diurnal cycle over land. The nonequilibrium regime occurs more frequently at mid-range wind speeds and with winds from southerly to westerly sectors. Most non-equilibrium convective events in the British Isles are initiated near large coastal orographic gradients or on the European continent. Thus, the convective adjustment timescale is greatest when the location being examined is immediately downstream of large orographic gradients and decreases with distance from the convective initiation region. The dominance of convective quasiequilibrium conditions over the British Isles argues for the use of large-member ensembles in probabilistic forecasts for this region.

Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.