Properties of singular vectors using convective available potential energy as final time norm

We study the feasibility of using the singular vector technique to create initial condition perturbations for short-range ensemble prediction systems (SREPS) focussing on predictability of severe local storms and in particular deep convection. For this a new final time semi-norm based on the convective available potential energy (CAPE) is introduced. We compare singular vectors using the CAPE-norm with SVs using the more common total energy (TE) norm for a 2-week summer period in 2007, which includes a case of mesoscale extreme rainfall in the south west of Finland. The CAPE singular vectors perturb the CAPE field by increasing the specific humidity and temperature of the parcel and increase the lapse rate above the parcel in the lower troposphere consistent with physical considerations. The CAPE-SVs are situated in the lower troposphere. This in contrast to TE-SVs with short optimization times which predominantly remain in the high troposphere. By examining the time evolution of the CAPE singular values we observe that the convective event in the south west of Finland is clearly associated with high CAPE singular values.

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.

Predicting outliers in ensemble forecasts

An ensemble forecast is a collection of runs of a numerical dynamical model, initialized with perturbed initial conditions. In modern weather prediction for example, ensembles are used to retrieve probabilistic information about future weather conditions. In this contribution, we are concerned with ensemble forecasts of a scalar quantity (say, the temperature at a specific location). We consider the event that the verification is smaller than the smallest, or larger than the largest ensemble member. We call these events outliers. If a K-member ensemble accurately reflected the variability of the verification, outliers should occur with a base rate of 2/(K + 1). In operational forecast ensembles though, this frequency is often found to be higher. We study the predictability of outliers and find that, exploiting information available from the ensemble, forecast probabilities for outlier events can be calculated which are more skilful than the unconditional base rate. We prove this analytically for statistically consistent forecast ensembles. Further, the analytical results are compared to the predictability of outliers in an operational forecast ensemble by means of model output statistics. We find the analytical and empirical results to agree both qualitatively and quantitatively.

The potential for skill across the range of the seamless weather-climate prediction problem: a stimulus for our science

Predictability is considered in the context of the seamless weather-climate prediction problem, and the notion is developed that there can be predictive power on all time-scales. On all scales there are phenomena that occur as well as longer time-scales and external conditions that should combine to give some predictability. To what extent this theoretical predictability may actually be realised and, further, to what extent it may be useful is not clear. However the potential should provide a stimulus to, and high profile for, our science and its application for many years.

Measuring significant variability characteristics: An assessment of three UK renewables

The variability of renewable energy is widely recognised as a challenge for integrating high levels of renewable generation into electricity systems. However, to explore its implications effectively, variability itself should first be clearly understood. This is particularly true for national electricity systems with high planned penetration of renewables and limited interconnection such as the UK. Variability cannot be considered as a distinct resource property with a single measurable parameter, but is a multi-faceted concept best described by a range of distinct characteristics. This paper identifies relevant characteristics of variability, and considers their implications for energy research. This is done through analysis of wind, solar and tidal current resources, with a primary focus on the Bristol Channel region in the UK. The relationship with electricity demand is considered, alongside the potential benefits of resource diversity. Analysis is presented in terms of persistence, distribution, frequency and correlation between supply and demand. Marked differences are seen between the behaviours of the individual resources, and these give rise to a range of different implications for system integration. Wind shows strong persistence and a useful seasonal pattern, but also a high spread in energy levels at timescales beyond one or two days. The solar resource is most closely correlated with electricity demand, but is undermined by night-time zero values and an even greater spread of monthly energy delivered than wind. In contrast, the tidal resource exhibits very low persistence, but also much greater consistency in energy values assessed across monthly time scales. Whilst this paper focuses primarily on the behaviour of resources, it is noted that discrete variability characteristics can be related to different system impacts. Persistence and predictability are relevant for system balancing, whereas statistical distribution is more relevant when exploring issues of asset utilisation and energy curtailment. Areas of further research are also identified, including the need to assess the value of predictability in relation to other characteristics.

Enhanced seasonal forecast skill following stratospheric sudden warmings

Advances in seasonal forecasting have brought widespread socio-economic benefits. However, seasonal forecast skill in the extratropics is relatively modest, prompting the seasonal forecasting community to search for additional sources of predictability. For over a decade it has been suggested that knowledge of the state of the stratosphere can act as a source of enhanced seasonal predictability; long-lived circulation anomalies in the lower stratosphere that follow stratospheric sudden warmings are associated with circulation anomalies in the troposphere that can last up to two months. Here, we show by performing retrospective ensemble model forecasts that such enhanced predictability can be realized in a dynamical seasonal forecast system with a good representation of the stratosphere. When initialized at the onset date of stratospheric sudden warmings, the model forecasts faithfully reproduce the observed mean tropospheric conditions in the months following the stratospheric sudden warmings. Compared with an equivalent set of forecasts that are not initialized during stratospheric sudden warmings, we document enhanced forecast skill for atmospheric circulation patterns, surface temperatures over northern Russia and eastern Canada and North Atlantic precipitation. We suggest that seasonal forecast systems initialized during stratospheric sudden warmings are likely to yield significantly greater forecast skill in some regions.

Understanding advances in the simulation of intraseasonal variability in the ECMWF model. Part I: the representation of the MJO

As a major mode of intraseasonal variability, which interacts with weather and climate systems on a near-global scale, the Madden – Julian Oscillation (MJO) is a crucial source of predictability for numerical weather prediction (NWP) models. Despite its global significance and comprehensive investigation, improvements in the representation of the MJO in an NWP context remain elusive. However, recent modifications to the model physics in the ECMWF model led to advances in the representation of atmospheric variability and the unprecedented propagation of the MJO signal through the entire integration period. In light of these recent advances, a set of hindcast experiments have been designed to assess the sensitivity of MJO simulation to the formulation of convection. Through the application of established MJO diagnostics, it is shown that the improvements in the representation of the MJO can be directly attributed to the modified convective parametrization. Furthermore, the improvements are attributed to the move from a moisture-convergent- to a relative-humidity-dependent formulation for organized deep entrainment. It is concluded that, in order to understand the physical mechanisms through which a relative-humidity-dependent formulation for entrainment led to an improved simulation of the MJO, a more process-based approach should be taken. T he application of process-based diagnostics t o t he hindcast experiments presented here will be the focus of Part II of this study.

Numerical prediction of atmospheric blocking-A case study

A series of numerical models have been used to investigate the predictability of atmospheric blocking for an episode selected from FGGE Special Observing Period I. Level II-b FGGE data have been used in the experiment. The blocking took place over the North Atlantic region and is a very characteristic example of high winter blocking. It is found that the very high resolution models developed at ECMWF, in a remarkable way manage to predict the blocking event in great detail, even beyond 1 week. Although models with much less resolution manage to predict the blocking phenomenon as such, the actual evolution differs very much from the observed and consequently the practical value is substantially reduced. Wind observations from the geostationary satellites are shown to have a substantial impact on the forecast beyond 5 days, as well as an extension of the integration domain to the whole globe. Quasi-geostrophic baroclinic models and, even more, barotropic models, are totally inadequate to predict blocking except in its initial phase. The prediction experiment illustrates clearly that efforts which have gone into the improvement of numerical prediction models in the last decades have been worth while.

Dynamics, stratospheric ozone, and climate change

Dynamics affects the distribution and abundance of stratospheric ozone directly through transport of ozone itself and indirectly through its effect on ozone chemistry via temperature and transport of other chemical species. Dynamical processes must be considered in order to understand past ozone changes, especially in the northern hemisphere where there appears to be significant low-frequency variability which can look “trend-like” on decadal time scales. A major challenge is to quantify the predictable, or deterministic, component of past ozone changes. Over the coming century, changes in climate will affect the expected recovery of ozone. For policy reasons it is important to be able to distinguish and separately attribute the effects of ozone-depleting substances and greenhouse gases on both ozone and climate. While the radiative-chemical effects can be relatively easily identified, this is not so evident for dynamics — yet dynamical changes (e.g., changes in the Brewer-Dobson circulation) could have a first-order effect on ozone over particular regions. Understanding the predictability and robustness of such dynamical changes represents another major challenge. Chemistry-climate models have recently emerged as useful tools for addressing these questions, as they provide a self-consistent representation of dynamical aspects of climate and their coupling to ozone chemistry. We can expect such models to play an increasingly central role in the study of ozone and climate in the future, analogous to the central role of global climate models in the study of tropospheric climate change.

Numerical prediction of atmospheric blocking - A case study

One of the fundamental questions in dynamical meteorology, and one of the basic objectives of GARP, is to determine the predictability of the atmosphere. In the early planning stage and preparation for GARP a number of theoretical and numerical studies were undertaken, indicating that there existed an inherent unpredictability in the atmosphere which even with the most ideal observing system would limit useful weather forecasting to 2-3 weeks.

Design of a forecasting system for ECMWF

As laid out in its convention there are 8 different objectives for ECMWF. One of the major objectives will consist of the preparation, on a regular basis, of the data necessary for the preparation of medium-range weather forecasts. The interpretation of this item is that the Centre will make forecasts once a day for a prediction period of up to 10 days. It is also evident that the Centre should not carry out any real weather forecasting but merely disseminate to the member countries the basic forecasting parameters with an appropriate resolution in space and time. It follows from this that the forecasting system at the Centre must from the operational point of view be functionally integrated with the Weather Services of the Member Countries. The operational interface between ECMWF and the Member Countries must be properly specified in order to get a reasonable flexibility for both systems. The problem of making numerical atmospheric predictions for periods beyond 4-5 days differs substantially from 2-3 days forecasting. From the physical point we can define a medium range forecast as a forecast where the initial disturbances have lost their individual structure. However we are still interested to predict the atmosphere in a similar way as in short range forecasting which means that the model must be able to predict the dissipation and decay of the initial phenomena and the creation of new ones. With this definition, medium range forecasting is indeed very difficult and generally regarded as more difficult than extended forecasts, where we usually only predict time and space mean values. The predictability of atmospheric flow has been extensively studied during the last years in theoretical investigations and by numerical experiments. As has been discussed elsewhere in this publication (see pp 338 and 431) a 10-day forecast is apparently on the fringe of predictability.

Seasonal persistence of midlatitude total ozone anomalies

Temporal autocorrelations of monthly mean total ozone anomalies over the 35–60°S and 35–60°N latitude bands reveal that anomalies established in the wintertime midlatitude ozone buildup persist (with photochemical decay) until the end of the following autumn, and then are rapidly erased once the next winter's buildup begins. The photochemical decay rate is found to be identical between the two hemispheres. High predictability of ozone through late summer exists based on the late-spring values. In the northern hemisphere, extending the 1979–2001 springtime ozone trend to other months through regression based on the seasonal persistence of anomalies captures the seasonality of the ozone trends remarkably well. In the southern hemisphere, the springtime trend only accounts for part of the summertime trends. There is a strong correlation between the ozone anomalies in northern hemisphere spring and those in the subsequent southern hemisphere spring, but not the converse.

Climate change scenarios for precipitation extremes in Portugal

Precipitation indices are commonly used as climate change indicators. Considering four Climate Variability and Predictability-recommended indices, this study assesses possible changes in their spatial patterns over Portugal under future climatic conditions. Precipitation data from the regional climate model Consortium for Small-Scale Modelling–Climate version of the Local Model (CCLM) ensemble simulations with ECHAM5/MPI-OM1 boundary conditions are used for this purpose. For recent–past, medians and probability density functions of the CCLM-based indices are validated against station-based and gridded observational dataset from ENSEMBLES-based (gridded daily precipitation data provided by the European Climate Assessment & Dataset project) indices. It is demonstrated that the model is able to realistically reproduce not only precipitation but also the corresponding extreme indices. Climate change projections for 2071–2100 (A1B and B1 SRES scenarios) reveal significant decreases in total precipitation, particularly in autumn over northwestern and southern Portugal, though changes exhibit distinct local and seasonal patterns and are typically stronger for A1B than for B1. The increase in winter precipitation over northeastern Portugal in A1B is the most important exception to the overall drying trend. Contributions of extreme precipitation events to total precipitation are also expected to increase, mainly in winter and spring over northeastern Portugal. Strong projected increases in the dry spell lengths in autumn and spring are also noteworthy, giving evidence for an extension of the dry season from summer to spring and autumn. Although no coupling analysis is undertaken, these changes are qualitatively related to modifications in the large-scale circulation over the Euro-Atlantic area, more specifically to shifts in the position of the Azores High and associated changes in the large-scale pressure gradient over the area.