• Energy Research

Abstract Air temperature is an effective predictor for electricity demand, especially during hot periods where the need of electric air conditioning can be high. This paper presents for the first time an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction. The retrospective seasonal climate forecasts provided by ECWMF (European Centre for Medium-Range Weather Forecasts) are used to forecast the June–July Italian electricity demand for the period 1990–2007. We find a relationship between summer (June–July) average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach. With the aim to evaluate the potential usefulness of the information contained into the climate ensemble forecast, the analysis is extended considering a probabilistic approach. Results show that, especially in the Center-South of Italy, seasonal forecasts of temperature issued in May lead to a significant correlation coefficient of electricity demand greater than 0.6 for the summer period. The average correlation obtained from seasonal forecasts is 0.53 for the temperature predicted in May and 0.19 for the predictions issued in April for the linear model, while the non-linear approach leads to the coefficients of 0.62 and 0.36 respectively. For the probabilistic approach, seasonal forecasts exhibit a positive and significant skill-score in predicting the demand above/below the upper/lower tercile in many regions. This work is a significant progress in understanding the relationship between temperature and electricity demand. It is shown that much of the predictable electricity demand anomaly over Italy is connected with so-called heat-waves (i.e. long lasting positive temperature anomalies) over Europe.

<div> <p>Changes in snow and vegetation cover associated with global warming can modify surface albedo (the reflected amount of radiative energy from the sun), therefore modulating the rise of surface temperature that is primarily caused by anthropogenic greenhouse-gases emission. This introduces a series of potential feedbacks <span>to</span> regional warming with positive<span> (negative)</span> feedback<span>s</span> enhancing<span> (reducing)</span> temperature increase by augmenting<span> (decreasing) the absorption of </span>short-wave radiation. So far our knowledge on the importance and magnitude of these feedbacks has been hampered by the limited availability of relatively long records of continuous satellite observations.</p> </div><div> <p>Here we exploit a 3<span>1</span>-year (1982-2012) high-frequency observational record of land data to quantify the strength of the surface-albedo feedback on land warming <span>modulated by snow and vegetation </span>during the recent historical period. To distinguish snow and vegetation contributions to this feedback, we examine temporal composites of satellite data in three different Northern Hemisphere domains. The analysis reveals and quantifies markedly different signatures of <span>the </span>surface-albedo feedback. A large positive surface-albedo feedback of<span> +0</span>.87 [CI 95%: 0.68, 1.05] W/(m<sup>2</sup>∗K) <span>absorb</span>ed solar radiation per degree of temperature increase is estimated in the domain where snow dominates. On the other hand the surface-albedo feedback becomes predominantly negative where vegetation dominates: it is largely negative (<span>-</span>0.91 [<span>-</span>0.81, <span>-</span>1.03] W/(m<sup>2</sup>∗K)) in the domain with vegetation dominating, while it is moderately negative (<span>-</span>0.57 [<span>-</span>0.40, <span>-</span>0.72] W/(m<sup>2</sup>∗K)) where both vegetation and snow are significantly present.  <span>S</span>now cover reduction consistently provides a positive feedback on warming<span>. In contrast,</span> vegetation<span> expansion</span> can produce <span>either</span> positive <span>or</span> negative feedbacks<span> in different regions and seasons, depending on whether the underlying surface being replaced has higher (e.g. snow) or lower (e.g. dark soils) albedo than vegetation.</span></p> <p><span>The observational data and analysis from this work is </span><span>supplying</span> fundamental knowledge to model and predict how <span>the </span>surface-albedo feedback will evolve and affect the rate of regional temperature rise in the future<span>. </span><span>So far the simulation and prediction of albedo feedbacks shows a large spread and divergence among the available state-of-the-art Earth System Models (ESMs), due to uncertainties in the representation of vegetation-snow processes and the dynamics of vegetation and to uncertainties in land-cover parameters. </span><span>By exploiting the</span><span> unprecedented observational benchmarks to evaluate the ESMs currently engaged in CMIP6, this work will allow an improved and better constrained representation of the processes underlying surface albedo feedbacks in the next generation of ESMs.</span> </p> </div><div> <p><span>This work is in now in Press and Open Access on Environmental Research Letters:</span> https://doi.org/10.1088/1748-9326/abd65f</p> </div>