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.

How will organic carbon stocks in mineral soils evolve under future climate? Global projections using RothC for a range of climate change scenarios

We use a soil carbon (C) model (RothC), driven by a range of climate models for a range of climate scenarios to examine the impacts of future climate on global soil organic carbon (SOC) stocks. The results suggest an overall global increase in SOC stocks by 2100 under all scenarios, but with a different extent of increase among the climate model and emissions scenarios. The impacts of projected land use changes are also simulated, but have relatively minor impacts at the global scale. Whether soils gain or lose SOC depends upon the balance between C inputs and decomposition. Changes in net primary production (NPP) change C inputs to the soil, whilst decomposition usually increases under warmer temperatures, but can also be slowed by decreased soil moisture. Underlying the global trend of increasing SOC under future climate is a complex pattern of regional SOC change. SOC losses are projected to occur in northern latitudes where higher SOC decomposition rates due to higher temperatures are not balanced by increased NPP, whereas in tropical regions, NPP increases override losses due to higher SOC decomposition. The spatial heterogeneity in the response of SOC to changing climate shows how delicately balanced the competing gain and loss processes are, with subtle changes in temperature, moisture, soil type and land use, interacting to determine whether SOC increases or decreases in the future. Our results suggest that we should stop looking for a single answer regarding whether SOC stocks will increase or decrease under future climate, since there is no single answer. Instead, we should focus on improving our prediction of the factors that determine the size and direction of change, and the land management practices that can be implemented to protect and enhance SOC stocks.

From short-range barotropic modelling to extended-range global weather prediction: a 40-year perspective

At the end of the 20th century, we can look back on a spectacular development of numerical weather prediction, which has, practically uninterrupted, been going on since the middle of the century. High-resolution predictions for more than a week ahead for any part of the globe are now routinely produced and anyone with an Internet connection can access many of these forecasts for anywhere in the world. Extended predictions for several seasons ahead are also being done — the latest El Niño event in 1997/1998 is an example of such a successful prediction. The great achievement is due to a number of factors including the progress in computational technology and the establishment of global observing systems, combined with a systematic research program with an overall strategy towards building comprehensive prediction systems for climate and weather. In this article, I will discuss the different evolutionary steps in this development and the way new scientific ideas have contributed to efficiently explore the computing power and in using observations from new types of observing systems. Weather prediction is not an exact science due to unavoidable errors in initial data and in the models. To quantify the reliability of a forecast is therefore essential and probably more so the longer the forecasts are. Ensemble prediction is thus a new and important concept in weather and climate prediction, which I believe will become a routine aspect of weather prediction in the future. The limit between weather and climate prediction is becoming more and more diffuse and in the final part of this article I will outline the way I think development may proceed in the future.

Impact of long-range correlations on trend detection in total ozone

Total ozone trends are typically studied using linear regression models that assume a first-order autoregression of the residuals [so-called AR(1) models]. We consider total ozone time series over 60°S–60°N from 1979 to 2005 and show that most latitude bands exhibit long-range correlated (LRC) behavior, meaning that ozone autocorrelation functions decay by a power law rather than exponentially as in AR(1). At such latitudes the uncertainties of total ozone trends are greater than those obtained from AR(1) models and the expected time required to detect ozone recovery correspondingly longer. We find no evidence of LRC behavior in southern middle-and high-subpolar latitudes (45°–60°S), where the long-term ozone decline attributable to anthropogenic chlorine is the greatest. We thus confirm an earlier prediction based on an AR(1) analysis that this region (especially the highest latitudes, and especially the South Atlantic) is the optimal location for the detection of ozone recovery, with a statistically significant ozone increase attributable to chlorine likely to be detectable by the end of the next decade. In northern middle and high latitudes, on the other hand, there is clear evidence of LRC behavior. This increases the uncertainties on the long-term trend attributable to anthropogenic chlorine by about a factor of 1.5 and lengthens the expected time to detect ozone recovery by a similar amount (from ∼2030 to ∼2045). If the long-term changes in ozone are instead fit by a piecewise-linear trend rather than by stratospheric chlorine loading, then the strong decrease of northern middle- and high-latitude ozone during the first half of the 1990s and its subsequent increase in the second half of the 1990s projects more strongly on the trend and makes a smaller contribution to the noise. This both increases the trend and weakens the LRC behavior at these latitudes, to the extent that ozone recovery (according to this model, and in the sense of a statistically significant ozone increase) is already on the verge of being detected. The implications of this rather controversial interpretation are discussed.

A two-tiered approach to long-range climate forecasting

Long-range global climate forecasts were made by use of a model for predicting a tropical Pacific sea-surface temperature (SST) in tandem with an atmospheric general circulation model. The SST is predicted first at long lead times into the future. These ocean forecasts are then used to force the atmospheric model and so produce climate forecasts at lead times of the SST forecasts. Prediction of seven large climatic events of the 1970s to 1990s by this technique are in good agreement with observations over many regions of the globe.

Medium-range forecasting at ECMWF: A review and comments on recent progress

The first ECMWF Seminar in 1975 (ECMWF, 1975) considered the scientific foundation of medium range weather forecasts. It may be of interest as a part of this lecture, to review some of the ideas and opinions expressed during this seminar.