70 research outputs found
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Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach
Soil moisture memory is a key component of seasonal predictability. However, uncertainty in current memory estimates is not clear and it is not obvious to what extent these are dependent on model uncertainties. To address this question, we perform a global sensitivity analysis of memory to key hydraulic parameters, using an uncoupled version of the H-TESSEL land surface model.
Results show significant dependency of estimates of memory and its uncertainty on these parameters, suggesting that operational seasonal forecasting models using deterministic hydraulic parameter values are likely to display a narrower range of memory than exists in reality. Explicitly incorporating hydraulic parameter uncertainty into models may then give improvements in forecast skill and reliability, as has been shown elsewhere in the literature. Our results also show significant differences with previous estimates of memory uncertainty, warning against placing too much confidence in a single quantification of uncertainty
Skilful probabilistic mediumârange precipitation and temperature forecasts over Vietnam for the development of a future dengue early warning system
Dengue fever is a source of substantial health burden in Vietnam. Given the wellâestablished influence of temperature and precipitation on vector biology and disease transmission, predictions of meteorological variables, such as those issued by ECMWF as a worldâleading provider of global ensemble forecasts, are likely to be valuable model inputs to a future dengue early warning system. In the absence of established verification at municipal and regional scales, this study assesses the skill of rainy season (MayâOctober) ensemble precipitation and 2âm temperature retrospective forecasts over North and South Vietnam initialized for dates during the period 2001â2020, evaluated against the ERA5 reanalysis for the same period. Forecasts are found to be significantly skilful compared with both climatology and persistence for lead times up to 10 days, including for cumulative precipitation values considered against independent rain gauge data. Rank histograms demonstrate that ensembles generally avoid excessive bias and consistently positive CRPSS values indicate substantial skill for temperature and cumulative precipitation forecasts for all spatial scales considered, despite differences in rainy season characteristics between North and South Vietnam. This forecast reliability demonstrates that meteorological input data based on ECMWF ensemble forecasts would add appreciably more value to the development of a future dengue early warning system compared to reference forecasts like climatology or persistence. These results raise hope for further exploration of predictive skill for relevant meteorological variables, particularly focused on their downscaling to produce districtâlevel epidemiological forecasts for urban areas where dengue is most prevalent
Beyond skill scores: exploring sub-seasonal forecast value through a case study of French month-ahead energy prediction
We quantify the value of sub-seasonal forecasts for a real-world prediction
problem: the forecasting of French month-ahead energy demand. Using surface
temperature as a predictor, we construct a trading strategy and assess the
financial value of using meteorological forecasts, based on actual energy
demand and price data. We show that forecasts with lead times greater than 2
weeks can have value for this application, both on their own and in conjunction
with shorter range forecasts, especially during boreal winter. We consider a
cost/loss framework based on this example, and show that while it captures the
performance of the short range forecasts well, it misses the marginal value
present in the longer range forecasts. We also contrast our assessment of
forecast value to that given by traditional skill scores, which we show could
be misleading if used in isolation. We emphasise the importance of basing
assessment of forecast skill on variables actually used by end-users.Comment: 22 pages, 8 figures, revised submission to QJRM
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Seasonal predictability of the winter North Atlantic Oscillation from a jet stream perspective
The winter North Atlantic Oscillation (NAO) has varied on interannual and decadal
timescales over the last century, associated with variations in the speed and latitude of the eddy-driven jet
stream. This paper uses hindcasts from two operational seasonal forecast systems (the European Centre for
Medium-range Weather Forecasts's seasonal forecast system, and the U.K. Met Office global seasonal
forecast system) and a century-long atmosphere-only experiment (using the European Centre for
Medium-range Weather Forecasts's Integrated Forecasting System model) to relate seasonal prediction
skill in the NAO to these aspects of jet variability. This shows that the NAO skill realized so far arises from
interannual variations in the jet, largely associated with its latitude rather than speed. There likely remains
further potential for predictability on longer, decadal timescales. In the small sample of models analyzed
here, improved representation of the structure of jet variability does not translate to enhanced seasonal
forecast skill
A Bayesian Approach to Atmospheric Circulation Regime Assignment
The standard approach when studying atmospheric circulation regimes and their
dynamics is to use a hard regime assignment, where each atmospheric state is
assigned to the regime it is closest to in distance. However, this may not
always be the most appropriate approach as the regime assignment may be
affected by small deviations in the distance to the regimes due to noise. To
mitigate this we develop a sequential probabilistic regime assignment using
Bayes Theorem, which can be applied to previously defined regimes and
implemented in real time as new data become available. Bayes Theorem tells us
that the probability of being in a regime given the data can be determined by
combining climatological likelihood with prior information. The regime
probabilities at time can be used to inform the prior probabilities at time
, which are then used to sequentially update the regime probabilities. We
apply this approach to both reanalysis data and a seasonal hindcast ensemble
incorporating knowledge of the transition probabilities between regimes.
Furthermore, making use of the signal present within the ensemble to better
inform the prior probabilities allows for identifying more pronounced
interannual variability. The signal within the interannual variability of
wintertime North Atlantic circulation regimes is assessed using both a
categorical and regression approach, with the strongest signals found during
very strong El Ni\~no years.Comment: Accepted for publication in Journal of Climat
Event attribution of a midlatitude windstorm using ensemble weather forecasts
The widespread destruction incurred by midlatitude storms every year makes it an imperative to study how storms change with climate. The impact of climate change on midlatitude windstorms, however, is hard to evaluate due to the small signals in variables such as wind speed, as well as the high resolutions required to represent the dynamic processes in the storms. Here, we assess how storm Eunice, which hit the UK in February 2022, was impacted by anthropogenic climate change using the ECMWF ensemble prediction system. This system was demonstrably able to predict the storm, significantly increasing our confidence in its ability to model the key physical processes and their response to climate change. Using modified greenhouse gas concentrations and changed initial conditions for ocean temperatures, we create two counterfactual scenarios of storm Eunice in addition to the forecast for the current climate. We compare the intensity and severity of the storm between the pre-industrial, current, and future climates. Our results robustly indicate that Eunice has become more intense with climate change and similar storms will continue to intensify with further anthropogenic forcing. These results are consistent across forecast lead times, increasing our confidence in them. Analysis of storm composites shows that this process is caused by increased vorticity production through increased humidity in the warm conveyor belt of the storm. This is consistent with previous studies on extreme windstorms. Our approach of combining forecasts at different lead times for event attribution enables combining event specificity and a focus on dynamic changes with the assessment of changing risks from windstorms. Further work is needed to develop methods to adjust the initial conditions of the atmosphere for the use in attribution studies using weather forecasts but we show that this approach is viable for reliable and fast attribution systems
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Improved seasonal prediction of the hot summer of 2003 over Europe through better representation of uncertainty in the land surface
Methods to explicitly represent uncertainties in weather and climate models have been developed and refined over the past decade, and have reduced biases and improved forecast skill when implemented in the atmospheric component of models. These methods have not yet been applied to the land surface component of models. Since the land surface is strongly coupled to the atmospheric state at certain times and in certain places (such as the European summer of 2003), improvements in the representation of land surface uncertainty may potentially lead to improvements in atmospheric forecasts for such events.
Here we analyse seasonal retrospective forecasts for 1981â2012 performed with the European Centre for Medium-Range Weather Forecastsâ (ECMWF) coupled ensemble forecast model. We consider two methods of incorporating uncertainty into the land surface model (H-TESSEL): stochastic perturbation of tendencies, and static perturbation of key soil parameters.
We find that the perturbed parameter approach considerably improves the forecast of extreme air temperature for summer 2003, through better representation of negative soil moisture anomalies and upward sensible heat flux. Averaged across all the reforecasts the perturbed parameter experiment shows relatively little impact on the mean bias, suggesting perturbations of at least this magnitude can be applied to the land surface without any degradation of model climate. There is also little impact on skill averaged across all reforecasts and some evidence of overdispersion for soil moisture.
The stochastic tendency experiments show a large overdispersion for the soil temperature fields, indicating that the perturbation here is too strong. There is also some indication that the forecast of the 2003 warm event is improved for the stochastic experiments, however the improvement is not as large as observed for the perturbed parameter experiment
Detection of interannual ensemble forecast signals over the North Atlantic and Europe using atmospheric circulation regimes
To study the forced variability of atmospheric circulation regimes, the use of model ensembles is often necessary for identifying statistically significant signals as the observed data constitute a small sample and are thus strongly affected by the noise associated with sampling uncertainty. However, the regime representation is itself affected by noise within the atmosphere, which can make it difficult to detect robust signals. To this end we employ a regularised k-means clustering algorithm to better identify the signal in a model ensemble. The approach allows for the identification of six regimes for the wintertime Euro-Atlantic sector and leads to more pronounced regime dynamics, compared to results without regularisation, both overall and on sub-seasonal and interannual time-scales. We find that sub-seasonal variability in the regime occurrence rates is mainly explained by changes in the seasonal cycle of the mean climatology. On interannual time-scales relations between the occurrence rates of the regimes and the El Niño Southern Oscillation (ENSO) are identified. The use of six regimes captures a more detailed response of the circulation to ENSO compared to the common use of four regimes. Predictable signals in occurrence rate on interannual time-scales are found for the two zonal flow regimes, namely a regime consisting of a negative geopotential height anomaly over the Norwegian Sea and Scandinavia, and the positive phase of the NAO. The signal strength for these regimes is comparable between observations and model, in contrast to that of the NAO-index where the signal strength in the observations is underestimated by a factor of 2 in the model. Our regime analysis suggests that this signal-to-noise problem for the NAO-index is primarily related to those atmospheric flow patterns associated with the negative NAO-index as we find poor predictability for the corresponding NAO (Formula presented.) regime
Atmospheric seasonal forecasts of the twentieth century: multi-decadal variability in predictive skill of the winter North Atlantic Oscillation (NAO) and their potential value for extreme event attribution
Based on skill estimates from hindcasts made over the last couple of decades, recent studies have suggested that considerable success has been achieved in forecasting winter climate anomalies over the Euro-Atlantic area using current-generation dynamical forecast models. However, previous-generation models had shown that forecasts of winter climate anomalies in the 1960s and 1970s were less successful than forecasts of the 1980s and 1990s. Given that the more recent decades have been dominated by the North Atlantic Oscillation (NAO) in its positive phase, it is important to know whether the performance of current models would be similarly skilful when tested over periods of a predominantly negative NAO. To this end, a new ensemble of atmospheric seasonal hindcasts covering the period 1900â2009 has been created, providing a unique tool to explore many aspects of atmospheric seasonal climate prediction. In this study we focus on two of these: multi-decadal variability in predicting the winter NAO, and the potential value of the long seasonal hindcast datasets for the emerging science of probabilistic event attribution. The existence of relatively low skill levels during the period 1950sâ1970s has been confirmed in the new dataset. The skill of the NAO forecasts is larger, however, in earlier and later periods. Whilst these inter-decadal differences in skill are, by themselves, only marginally statistically significant, the variations in skill strongly co-vary with statistics of the general circulation itself suggesting that such differences are indeed physically based. The mid-century period of low forecast skill coincides with a negative NAO phase but the relationship between the NAO phase/amplitude and forecast skill is more complex than linear. Finally, we show how seasonal forecast reliability can be of importance for increasing confidence in statements of causes of extreme weather and climate events, including effects of anthropogenic climate change
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