A5085687655- Climate variability and models
- Meteorological Phenomena and Simulations
- Fire effects on ecosystems
- Climate Change Policy and Economics
- Atmospheric and Environmental Gas Dynamics
- Fire dynamics and safety research
- Hydrology and Drought Analysis
- Energy, Environment, and Transportation Policies
- Landslides and related hazards
- Climate change impacts on agriculture
- Fire Detection and Safety Systems
- Computational Physics and Python Applications
- Economic and Environmental Valuation
- Generative Adversarial Networks and Image Synthesis
- Climate Change and Health Impacts
- Forecasting Techniques and Applications
- Precipitation Measurement and Analysis
- Decision-Making and Behavioral Economics
- Flood Risk Assessment and Management
- Food Industry and Aquatic Biology
- Hydrological Forecasting Using AI
- Arctic and Antarctic ice dynamics
University of Reading
2023-2025
National Institute of Meteorology
2023-2024
New College
2022
University of Oxford
2022
University of Edinburgh
2018
Abstract. Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society environment. However, our understanding global distribution extreme fires remains skewed, primarily influenced by media coverage regionalised research efforts. This inaugural State Wildfires report systematically analyses fire activity worldwide, identifying events from March 2023–February 2024 season. We assess causes, predictability, attribution these...
Abstract. Large-scale atmospheric dynamics modulate the occurrence of extreme precipitation events and provide sources predictability these on timescales ranging from days to decades. In midlatitudes, dynamical drivers are frequently represented as discrete, persistent recurrent circulation regimes. However, available methods identify regimes which either predictable but not necessarily informative relevant local-scale impact studied, or targeted a no longer predictable. this paper, we...
Studying teleconnections using data-driven methods relies on identifying suitable representations of the relevant dynamical processes involved. Often, these are identified through a dimensionality reduction process itself, such as Niño3.4 index to represent El-Niño Southern Oscillation or clustering circulation regimes states North Atlantic eddy-driven jet. The relationship between can subsequently be assessed in causal model. However, since independently teleconnection...
Statistical bias adjustment of climate models has become widespread practice to bridge the usability gap information for impact studies and other societal applications. However, application offers potential misuse comes with several fundamental issues which have been highlighted in literature. In this tension between use issues, different strategies statistical developed, ranging from selecting a consistent method across applications ensure comparability, applying an ensemble available...
At subseasonal to seasonal lead times, the forecast skill of extreme events is known be intermittent and dependent on specific phenomena or conditions, such as a strong El Niño event sudden stratospheric warming. These states enhanced predictability in climate system are termed windows opportunity. Although this concept widely recognised, diagnosing opportunity remains an issue often relies evaluating conditional model skill, thereby conflating window with ability represent it....
Subseasonal-to-seasonal (S2S) forecasts are crucial for decision-making and early warning systems in extreme weather. However, the chaotic nature of atmospheric dynamics limits predictive skill climate models on S2S timescales. Teleconnections can provide windows improved predictability, but leveraging these external drivers to enhance forecast remains challenging. This study introduces a spatio-temporal neural network (STNN) designed predict weekly North Atlantic European (NAE) weather...
Abstract. Statistical bias adjustment is commonly applied to climate models before using their results in impact studies. However, different methods based on a distributional mapping between observational and model data can change the simulated trends as well spatiotemporal inter-variable consistency of model, are prone misuse if not evaluated thoroughly. Despite importance these fundamental issues, researchers who apply currently do have tools at hand compare or evaluate sufficiently detect...
© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).A supplement to article is available online (10.1175/BAMS-D-17-0096.2)
Abstract. Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society environment. However, our understanding global distribution extreme fires remains skewed, primarily influenced by media coverage regional research concentration. This inaugural State Wildfires report systematically analyses fire activity worldwide, identifying events from March 2023–February 2024 season. We assess causes, predictability, attribution these to climate...
Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL Steering the Climate System: An Extended Comment 36 Pages Posted: 21 Feb 2019 See all articles by Linus MattauchLinus MattauchUniversity of OxfordRichard MillarUniversity OxfordRick van der PloegUniversity OxfordArmon RezaiVienna University Economics and BusinessAnselm SchultesPotsdam-Institut für Klimafolgenforschung (PIK)Frank VenmansUniversité de...
Abstract. Statistical bias adjustment is commonly applied to climate models before using their results in impact studies. However, different methods, based on a distributional mapping between observational and model data, can change the simulated trends, as well spatiotemporal inter-variable consistency of model, are prone misuse if not evaluated thoroughly. Despite importance these fundamental issues, researchers who apply currently do have tools at hand compare methods or evaluate...
Weather regimes are recurrent and persistent large-scale atmospheric circulation patterns that modulate the occurrence of local impact variables such as extreme precipitation. In their capacity mediators between long-range teleconnections these extremes, they have shown potential for improving sub-seasonal forecasting well long-term climate projections. However, existing methods identifying weather not designed to capture physical processes relevant variable in question while still...
Abstract Large-scale atmospheric circulation patterns, so-called weather regimes, modulate the occurrence of extreme events such as heatwaves or precipitation. In their role mediators between long-range teleconnections and local impacts, regimes have demonstrated potential in improving long-term climate projections well sub-seasonal to seasonal forecasts. However, existing methods for identifying are not specifically designed capture relevant physical processes responsible variations impact...
Statistical bias adjustment is now common practice when using climate models for impact studies, prior to or in conjunction with downscaling methods. Examples of widely used methodologies include CDFt (Vrac et al. 2016), ISIMIP3BASD (Lange 2019) equidistant CDF matching (Li 2010). Though practice, recent papers (Maraun 2017) have found fundamental issues statistical adjustment. When multivariate aspects are not evaluated, improper use detected. Fundamental misspecifications the model, such...