Dr Antje Weisheimer (she)

Abstract:

We propose a new temporal merging method for seamless climate forecast information of climate extremes. In order to construct stable and seamless climate forecast information for user-oriented climate extremes, ensemble members are pooled across available start dates using relative weights, for a target season. Recently, vineyard farmers from Catalonia, Spain, reported frost risk during the spring months (March and April). The observations show high interannual variability of the frequency of frost days (FFD) over Catalonia for the period 1983–2022. Multi-annual to seasonal forecasts from the European Centre for Medium-Range Weather Forecasts show a varying level of FFD forecast skill for lead times 1 to 22 months. The temporally merged weighted forecast enhances forecast skill compared to individual start dates, by adding information from previous start dates, at no additional computational cost. For most start dates, the skill is primarily attributed to the warming trends, except for a few start dates, where remanent forecast skill is attributed to internal variability. The proposed anticipatory framework “Ready-Steady-Action/Go” shows that adopting seamless climate forecast information can bring about 20 to 30% potential economic value for vineyard farmers, with a maximum value noted for multi-annual forecasts.

Abstract:

Since 2004, many methods for event attribution have been developed. Early studies showed that attribution statements are sensitive to the framing of research questions but few large comparisons have been undertaken.Here, we firstly motivate the need for multi-method extreme event attribution, highlighting conceptual differences between methods. In a second part, we present a case study of midlatitude storm Babet (2023) to compare three common storyline attribution methods, alongside a severity-based probabilistic method. We discuss three widely relevant questions which highlight the complementarity and the differences between methods: (1) How has climate change impacted the frequency of the event? (2) How has climate change impacted the event severity? (3) Were the dynamics of the event influenced by climate change and if yes, how?We show that methods differ in the extent to which they reproduce observed weather patterns. This influences attribution statements, and can even change the sign of results for events with uncertain climate signals. We argue that limitations and strengths of methods need to be clearly communicated when presenting event attribution reports to ensure findings can be used reliably by a wide range of stakeholders.

Abstract:

ABSTRACT Dengue fever outbreaks impose a severe healthcare burden in Vietnam; therefore, the development of a Dengue early warning system is key to improve public health planning and mitigate this burden. This study assesses the ECMWF medium‐range (up to 10 days) forecast skill for relative humidity in Vietnam—a key factor for vector‐borne disease transmission—in re‐forecasts between 2001 and 2020. Analysis focused on the rainy season (May–October) with ERA5 reanalysis as a reference dataset. Re‐forecast data were pre‐processed using a lead‐time dependent quantile mapping technique to reduce the bias between forecasted and observational data, and skill was assessed using climatology and persistence as a reference. Rank histograms showed that the humidity forecast is reliable up to 10 days, and continuous ranked probability skill score (CRPSS) values show that the forecast is more skilful than the climatology up to 10 days. Nonetheless, when using persistence as a reference, CRPSS values are lower in South Vietnam, which was associated with the inaccurate representation of 2 m dew point temperature in the tropical regions, and the fact that persistence is a hard reference to beat in the tropics, hindering model forecast skill. Results from this study demonstrate that ECMWF ensemble forecasts of relative humidity are suitable to use as inputs for a Dengue early warning system up to 10 days in advance.