Abstract

The benefits of potential electric power transfers between the Pacific Northwest (PNW) and California (CA) are evaluated using a linked set of hydrologic, reservoir, and power demand simulation models for the Columbia River and the Sacramento–San Joaquin reservoir systems. The models provide a framework for evaluating climate-related variations and long-range predictability of regional electric power demand, hydropower production, and the benefits of potential electric power transfers between the PNW and CA. The period of analysis is 1917–2002. The study results show that hydropower production and regional electric power demands in the PNW and CA are out of phase seasonally but that hydropower productions in the PNW and CA have strongly covaried on an annual basis in recent decades. Winter electric power demand and spring and annual hydropower production in the PNW are related to both El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) through variations in winter climate. Summer power demand in CA is related primarily to variations in the PDO in spring. Hydropower production in CA, despite recent covariation with the PNW, is not strongly related to ENSO variability overall. Primarily because of strong variations in supply in the PNW, potential hydropower transfers between the PNW and CA in spring and summer are shown to be correlated to ENSO and PDO, and the conditional probability distributions of these transfers are therefore predictable with long lead times. Such electric power transfers are estimated to have potential average annual benefits of $136 and $79 million for CA and the PNW, respectively, at the year-2000 regional demand level. These benefits are on average 11%–27% larger during cold ENSO/PDO events and are 16%–30% lower during warm ENSO/PDO events. Power transfers from the PNW to CA and hydropower production in CA are comparable in magnitude, on average.

The Baltic Sea Experiment (BALTEX) is one of the five continental-scale experiments of the Global Energy and Water Cycle Experiment (GEWEX). More than 50 research groups from 14 European countries are participating in this project to measure and model the energy and water cycle over the large drainage basin of the Baltic Sea in northern Europe. BALTEX aims to provide a better understanding of the processes of the climate system and to improve and to validate the water cycle in regional numerical models for weather forecasting and climate studies. A major effort is undertaken to couple interactively the atmosphere with the vegetated continental surfaces and the Baltic Sea including its sea ice. The intensive observational and modeling phase BRIDGE, which is a contribution to the Coordinated Enhanced Observing Period of GEWEX, will provide enhanced datasets for the period October 1999–February 2002 to validate numerical models and satellite products. Major achievements have been obtained in an improved understanding of related exchange processes. For the first time an interactive atmosphere–ocean–land surface model for the Baltic Sea was tested. This paper reports on major activities and some results.

Abstract

Pan-Africa convection-permitting regional climate model simulations have been performed to study the impact of high resolution and the explicit representation of atmospheric moist convection on the present and future climate of Africa. These unique simulations have allowed European and African climate scientists to understand the critical role that the representation of convection plays in the ability of a contemporary climate model to capture climate and climate change, including many impact-relevant aspects such as rainfall variability and extremes. There are significant improvements in not only the small-scale characteristics of rainfall such as its intensity and diurnal cycle, but also in the large-scale circulation. Similarly, effects of explicit convection affect not only projected changes in rainfall extremes, dry spells, and high winds, but also continental-scale circulation and regional rainfall accumulations. The physics underlying such differences are in many cases expected to be relevant to all models that use parameterized convection. In some cases physical understanding of small-scale change means that we can provide regional decision-makers with new scales of information across a range of sectors. We demonstrate the potential value of these simulations both as scientific tools to increase climate process understanding and, when used with other models, for direct user applications. We describe how these ground-breaking simulations have been achieved under the U.K. Government’s Future Climate for Africa Programme. We anticipate a growing number of such simulations, which we advocate should become a routine component of climate projection, and encourage international coordination of such computationally and human-resource expensive simulations as effectively as possible.

Abstract

The subseasonal-to-seasonal (S2S) predictive time scale, encompassing lead times ranging from 2 weeks to a season, is at the frontier of forecasting science. Forecasts on this time scale provide opportunities for enhanced application-focused capabilities to complement existing weather and climate services and products. There is, however, a “knowledge–value” gap, where a lack of evidence and awareness of the potential socioeconomic benefits of S2S forecasts limits their wider uptake. To address this gap, here we present the first global community effort at summarizing relevant applications of S2S forecasts to guide further decision-making and support the continued development of S2S forecasts and related services. Focusing on 12 sectoral case studies spanning public health, agriculture, water resource management, renewable energy and utilities, and emergency management and response, we draw on recent advancements to explore their application and utility. These case studies mark a significant step forward in moving from potential to actual S2S forecasting applications. We show that by placing user needs at the forefront of S2S forecast development—demonstrating both skill and utility across sectors—this dialogue can be used to help promote and accelerate the awareness, value, and cogeneration of S2S forecasts. We also highlight that while S2S forecasts are increasingly gaining interest among users, incorporating probabilistic S2S forecasts into existing decision-making operations is not trivial. Nevertheless, S2S forecasting represents a significant opportunity to generate useful, usable, and actionable forecast applications for and with users that will increasingly unlock the potential of this forecasting time scale.