The North American Multimodel Ensemble prediction experiment is described, and forecast quality and methods for accessing digital and graphical data from the model are discussed.

While seasonal outlooks have been operational for many years, until recently the extended‐range timescale referred to as subseasonal‐to‐seasonal (S2S) has received little attention. S2S prediction fills the gap between short‐range weather prediction and long‐range seasonal outlooks. Decisions in a range of sectors are made in this extended‐range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications‐relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision‐making and valuation of forecasts, to enhance the benefits of ‘climate services’ approaches for extended‐range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application‐ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.

The present study compares the local simultaneous correlation between rainfall–evaporation and sea surface temperature (SST)–SST tendency among observations, coupled general circulation model (CGCM) simulations, and stand-alone atmospheric general circulation model (AGCM) simulations. The purpose is to demonstrate to what extent the model simulations can reproduce the observed air–sea relationship. While the model-simulated correlation agrees with the observations in the tropical eastern Pacific, large discrepancies are found in the subtropics, midlatitudes, and tropical Indo-western Pacific Ocean regions. In tropical Indo-western Pacific Ocean regions and the midlatitudes where the atmosphere contributes to the observed SST changes, the specified SST simulations produce excessive SST forcing, whereas the CGCM captures the atmospheric feedback on the SST, but with somewhat of an overestimation. In the subtropics, both the AGCM and CGCM produce unrealistic positive rainfall–SST correlations. In the tropical western-central Pacific and the North Indian Ocean, the CGCM-simulated evaporation–SST correlation is opposite to that observed because of an excessive dependence of the sea–air humidity difference on the SST.