[1] The second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) is aimed at quantifying, with a suite of long-range forecast systems, the degree to which realistic land surface initialization contributes to the skill of subseasonal precipitation and air temperature forecasts. Results, which focus here on North America, show significant contributions to temperature prediction skill out to two months across large portions of the continent. For precipitation forecasts, contributions to skill are much weaker but are still significant out to 45 days in some locations. Skill levels increase markedly when calculations are conditioned on the magnitude of the initial soil moisture anomaly. Citation: Koster, R. D., et al.

[1] A method for post-processing decadal predictions from global climate models that accounts for model deficiencies in representing climate trends is proposed and applied to decadal predictions of annual global mean temperature from the Canadian Centre for Climate Modelling and Analysis climate model. The method, which provides a time-dependent trend adjustment, reduces residual drifts that remain after applying the standard time-independent bias correction when the modelled and observed long-term trends differ. Initialized predictions and uninitialized simulations that share common specified external forcing are analyzed. Trend adjustment substantially reduces forecast errors in both cases and initialization further enhances skill, particularly for the first forecast year.

[1] The utility of multi-system, coupled model-based seasonal predictions of Arctic sea ice area and extent is investigated for combined predictions from the Climate Forecast System version 2 (CFSv2) and Canadian Seasonal to Interannual Prediction System (CanSIPS) operational seasonal forecasting systems, which are among the first to have sea ice as a prognostic variable. Forecast skills for predictions of total anomalies and departures from long-term linear trends are examined both for the individual systems and the combined forecasts, and are compared against simple predictions such as damped anomaly persistence. Results indicate that the tendency for climate forecasts based on combined output from multiple prediction systems to outperform any one system, demonstrated previously for global variables such as temperature and precipitation, is realized for predictions of Arctic sea ice as well.

We introduce the Clouds Above the United States and Errors at the Surface (CAUSES) project with its aim of better understanding the physical processes leading to warm screen temperature biases over the American Midwest in many numerical models. In this first of four companion papers, 11 different models, from nine institutes, perform a series of 5 day hindcasts, each initialized from reanalyses. After describing the common experimental protocol and detailing each model configuration, a gridded temperature data set is derived from observations and used to show that all the models have a warm bias over parts of the Midwest. Additionally, a strong diurnal cycle in the screen temperature bias is found in most models. In some models the bias is largest around midday, while in others it is largest during the night. At the Department of Energy Atmospheric Radiation Measurement Southern Great Plains (SGP) site, the model biases are shown to extend several kilometers into the atmosphere. Finally, to provide context for the companion papers, in which observations from the SGP site are used to evaluate the different processes contributing to errors there, it is shown that there are numerous locations across the Midwest where the diurnal cycle of the error is highly correlated with the diurnal cycle of the errorThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

We compare observed decadal trends in global mean surface temperature with those predicted using a modelling system that encompasses observed initial condition information, externally forced response (due to anthropogenic greenhouse gases and aerosol precursors), and internally generated variability. We consider retrospective decadal forecasts for nine cases, initiated at five year intervals, with the first beginning in 1961 and the last in 2001. Forecast ensembles of size thirty are generated from differing but similar initial conditions. We concentrate on the trends that remain after removing the following natural signals in observations and hindcasts: dynamically induced atmospheric variability, El Niño‐Southern Oscillation (ENSO), and the effects of explosive volcanic eruptions. We show that ensemble mean errors in the decadal trend hindcasts are smaller than in a parallel set of uninitialized free running climate simulations. The ENSO signal, which is skillfully predicted out to a year or so, has little impact on our decadal trend predictions, and our modelling system possesses skill, independent of ENSO, in predicting decadal trends in global mean surface temperature.