Dr Antje Weisheimer (she)

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.

Abstract:

Seasonal forecasting systems have been operational for over two decades. Here we present a systematic analysis of the performance of operational seasonal forecasting models since their inception. We analyse seasonal forecasting systems from three major international operational centres that have produced and coordinated continuously on operational seasonal forecasts over the past 20 years. Due to the small sample size of available forecasts, it is difficult to draw meaningful conclusions using historical operational forecasts alone, therefore we focus primarily on available model hindcasts. Our analysis, which accounts for differences in ensemble size and period across the forecasting systems, demonstrates that there have been clear improvements in some regions through the different model eras. For both the boreal winter and summer hindcasts, there have been significant improvements in forecasting the tropical regions, which are concurrent with improvements in the skill of tropical sea‐surface temperature (SST) forecasts. These improvements in the Tropics are associated with increased predictability of temperature and precipitation across various continental regions on seasonal timescales. For the extratropics, the picture is more mixed, with strong improvements only evident during the boreal winter season over the North Pacific and North America. The sources of improvement over the winter extratropics are found to be strongly related to improvements in tropical SST skill and related improvements in the strength of the El Niño/Southern Oscillation (ENSO) teleconnection to the Pacific/North America pattern (PNA). Improvements of seasonal forecast skill over the rest of the extratropics, such as over Eurasia, are generally absent or patchy in individual models. The improvements that are found are most pronounced in the newest era models and are broadly associated with improvements in atmospheric model resolution. These improvements in skill are also evident in representative multi‐model ensembles that represent more closely how operational forecasts are used in practice.

Abstract:

While it is widely believed that the intense rainfall in summer 2022 over Pakistan was substantially exacerbated by anthropogenic climate change1, 2, climate models struggled to confirm this3, 4. Using a high-resolution operational seasonal forecasting system that successfully predicted the extreme wet conditions, we perform counterfactual experiments simulating pre-industrial and future conditions. Both experiments also exhibit strong anomalous rainfall, indicating a limited role of CO2-induced forcing. We attribute 10% of the total rainfall to historical increases in CO2 and ocean temperature. However, further increases in the future suggest a weak mean precipitation reduction but with increased variability. By decomposing rainfall and large-scale circulation into CO2 and SST-related signals, we illustrate a tendency for these signals to compensate each other in future scenarios. This suggests that historical CO2 impacts may not reliably predict future responses. Accurately capturing local dynamics is therefore essential for regional climate adaptation planning and for informing loss and damage discussions.