Dr Antje Weisheimer (she)

Abstract:

An anomalous signal-to-noise ratio (also called the signal-to-noise paradox) present in climate models has been widely reported, affecting predictions and projections from seasonal to centennial timescales and encompassing prediction skill from internal processes and external climate forcing. An anomalous signal-to-noise ratio describes a situation where the mean of a forecast ensemble correlates better with the corresponding verification than with its individual ensemble members. This situation has severe implications for climate science, meaning that large ensembles might be required to extract prediction signals. Although a number of possible physical mechanisms for this paradox have been proposed, none has been universally accepted. From a statistical point of view, an anomalous signal-to-noise ratio indicates that forecast ensemble members are not statistically interchangeable with the verification, and an apparent paradox arises only if such an interchangeability is assumed. It will be demonstrated in this study that an anomalous signal-to-noise ratio is a consequence of the relative magnitudes of the variance of the observations, the ensemble mean, and the error of the ensemble mean. By analysing the geometric triangle formed by these three quantities, and given that for typical seasonal forecasting systems both the correlation and the forecast signal are relatively small, it is concluded that an anomalous signal-to-noise ratio should, in fact, be expected in such circumstances.

Abstract:

Heatwaves constitute a major threat to human health and ecosystems. Projected increases in heatwave frequency and severity thus lead to the need for prediction to enhance preparedness and minimize adverse impacts. In this Review, we document current capabilities for heatwave prediction at daily to decadal timescales and outline projected changes under anthropogenic warming. Various local and remote drivers and feedbacks influence heatwave development. On daily timescales, extratropical atmospheric blocking and global land–atmosphere coupling are most pertinent, and on subseasonal to seasonal timescales, soil moisture and ocean surface anomalies contribute. Knowledge of these drivers allows heatwaves to be skilfully predicted at daily to weekly lead times. Predictions are challenging beyond timescales of a few weeks, but tendencies for above-average temperatures can be estimated. Further into the future, heatwaves are anticipated to become more frequent, persistent and intense in nearly all inhabited regions, with trends amplified by soil drying in some areas, especially the mid-latitudes. There is also an increased occurrence of humid heatwaves, especially in southern Asia. A better understanding of the relevant drivers and their model representation, including atmospheric dynamics, atmospheric and soil moisture, and surface cover should be prioritized to improve heatwave prediction and projection.

Abstract:

Despite the growing demand for long-range ENSO predictions beyond one year, quantifying the skill at these lead-times remains limited. This is partly due to inadequate long-records of seasonal reforecasts that make skill estimates of irregular ENSO events quite challenging. Here, we investigate ENSO predictability and the dependency of prediction skill on the ENSO cycle using 110-years of 24-month-long 10-member ensemble reforecasts from ECMWF’s coupled model (SEAS5-20C) initialised on 1st Nov/1st May during 1901-2010. Results show that Nino3.4 SST can be skilfully predicted up to ~18 lead months when initialised on 1st Nov, but skill drops at ~12 lead months for May starts that encounter boreal spring predictive barrier in year 2. The skill beyond the first year is highly conditioned to the phase of ENSO: Forecasts initialised at peak El Niño are more skilful in year 2 than those initialised at peak La Niña, with the transition to La Niña being more predictable than to El Niño. This asymmetry is related to the subsurface initial conditions in the western equatorial Pacific: peak El Niño states evolving into La Niña are associated with strong upper ocean heat discharge of the western Pacific, the memory of which stays beyond one year. In contrast, the western Pacific recharged state associated with La Niña is usually weaker and shorter-lived, being a weaker pre-conditioner for subsequent El Niño, the year after. High prediction skill of ENSO events beyond one year provides motivation for extending the lead-time of operational seasonal forecasts up to 2 years.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>Since the 1980s, external forcing from increasing greenhouse gases and declining aerosols has had a large e ect on European summer temperatures. The forcing may therefore provide an important source of forecast skill, even for timescales as short as a season ahead. However, the relative importance of such forcing for seasonal forecasts has thus far not been quanti ed, particularly on a regional scale. In this study, we investigate forcing-induced skill by comparing the temperature skill of a multi-model ensemble of operational seasonal predictions from the Copernicus Climate Change Service (C3S) archive to that of an uninitialised ensemble of CMIP6 projections for European summers spanning the years 1993-2016. We show that in some regions, such as northern Europe, summer 2m temperature skill is relatively limited and the forced trend provides the primary source of skill in current seasonal forecast models at 2-4 month lead-times. Over large parts of northern Europe, summer temperature skill is actually higher in uninitialised predictions and in runs with long lead-times than at short lead-times suggesting that there may be problems with the initialisation. Conversely, 2m temperature in southern Europe is generally well predicted by seasonal forecast models out to 3-5 months due to a combination of dynamical skill and a strong forced trend. These results indicate that even uninitialised predictions can provide useful information for seasonal forecasts of European summer temperatures and secondly that the ability of models to capture dynamical signals for northern European summers requires further research.</jats:p>