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This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)—e.g. quantile mapping—to more sophisticated ensemble recalibration (RC) methods—e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value—with respect to the raw model outputs—beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods.

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992–2010 period performed by five different global coupled ocean–atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land–atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.

A new 46‐year hindcast dataset for seasonal‐to‐annual ensemble predictions has been created using a multi‐model ensemble of 5 state‐of‐the‐art coupled atmosphere‐ocean circulation models. The multi‐model outperforms any of the single‐models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead‐times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi‐model ensemble than for DEMETER in the 4–6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi‐model ensemble does not improve the forecast skill further. Annual‐range hindcasts show anomaly correlation skill of ∼0.5 up to 14 months ahead. A wide range of output from the multi‐model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data.

The ENSEMBLES multi-model and perturbed-parameter decadal re-forecasts are used to assess multi-year forecast quality for global-mean surface air temperature (SAT) and North Atlantic multi-decadal sea surface temperature variability (AMV). Two issues for near-term climate prediction, not discussed so far, are addressed with these two examples: the impact of the choice of the observational reference period, and of the number of years included in the forecast average. Taking into account only years when both observational and model data are available, instead of using the full record, to estimate observed climatologies produces systematically (although not statistically significantly different) higher ensemble-mean correlations and lower root mean square errors in all forecast systems. These differences are more apparent in the second half of the decadal prediction, which suggests an influence of non-stationary long-term trends. Also, as the forecast period averaged increases, the correlation for both global-mean SAT and AMV is generally higher. This also suggests an increasing role for the variable external forcing as when forecast period averaged increases, unpredictable internal variability is smoothed out. The results show that predicting El Niño-Southern Oscillation beyond one year is a hurdle for current global forecast systems, which explains the positive impact of the forecast period averaging. By comparing initialized and uninitialized re-forecasts, the skill assessment confirms that variations of the global-mean SAT are largely controlled by the prescribed variable external forcing. By contrast, the initialization improves the skill of the AMV during the first half of the forecast period. In an operational context, this would lead to improved predictions of the AMV from initializing internal climate fluctuations. The coherence between the multi-model and perturbed-parameter ensemble supports that conclusion for boreal summer and annual means, while the results show less consistency for boreal winter.

Climate observations, research, and models are used extensively to help understand key processes underlying changes to the climate on a range of time scales from months to decades, and to investigate and describe possible longer-term future climates. The knowledge generated serves as a scientific basis for climate services that are provided with the aim of tailoring information for decision-makers and policy-makers. Climate models and climate services are crucial elements for supporting policy and other societal actions to mitigate and adapt to climate change, and for making society better prepared and more resilient to climate-related risks. We present recommendations for future research topics for climate modeling and for climate services. These recommendations were produced by a group of experts in climate modeling and climate services, selected based on their individual leadership roles or participation in international activities. The recommendations were reached through extensive analysis, consideration and discussion of current and desired research capabilities, and wider engagement and refinement of the recommendations was achieved through a targeted workshop of initial recommendations and an open meeting at the European Geosciences Union General Assembly. The findings emphasize how research and innovation activities in the fields of climate modeling and climate services can contribute to improving climate knowledge and information with saliency for users in order to enhance capacity to transition to a sustainable and resilient society. The findings are relevant worldwide but are deliberately intended to influence the European Commission’s next major multi-annual framework program of research and innovation over the period 2021–27.

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004–2009, with either realistic initialization of snow variables based on re-analyses, or else with “scrambled” snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This “warm Arctic—cold continent” difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses.

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.

The control of epidemic malaria is a priority for the international health community and specific targets for the early detection and effective control of epidemics have been agreed. Interannual climate variability is an important determinant of epidemics in parts of Africa where climate drives both mosquito vector dynamics and parasite development rates. Hence, skilful seasonal climate forecasts may provide early warning of changes of risk in epidemic-prone regions. Here we discuss the development of a system to forecast probabilities of anomalously high and low malaria incidence with dynamically based, seasonal-timescale, multi-model ensemble predictions of climate, using leading global coupled ocean-atmosphere climate models developed in Europe. This forecast system is successfully applied to the prediction of malaria risk in Botswana, where links between malaria and climate variability are well established, adding up to four months lead time over malaria warnings issued with observed precipitation and having a comparably high level of probabilistic prediction skill. In years in which the forecast probability distribution is different from that of climatology, malaria decision-makers can use this information for improved resource allocation.

The probabilistic skill of ensemble forecasts for the first month and the first season of the forecasts is assessed, where model uncertainty is represented by the a) multi‐model, b) perturbed parameters, and c) stochastic parameterisation ensembles. The main foci of the assessment are the Brier Skill Score for near‐surface temperature and precipitation over land areas and the spread‐skill relationship of sea surface temperature in the tropical equatorial Pacific. On the monthly timescale, the ensemble forecast system with stochastic parameterisation provides overall the most skilful probabilistic forecasts. On the seasonal timescale the results depend on the variable under study: for near surface temperature the multi‐model ensemble is most skilful for most land regions and for global land areas. For precipitation, the ensemble with stochastic parameterisation most often produces the highest scores on global and regional scales. Our results indicate that stochastic parameterisations should now be developed for multi‐decadal climate predictions using earth‐system models. Key Points Stochastic parametrisations produce best overall monthly forecast skill Seasonal temperature forecasts from a multi‐model ensemble perform best globally Seasonal precipitation forecasts using stochastic processes have highest skill

There is a growing need for skilful predictions of climate up to a decade ahead. Decadal climate predictions show high skill for surface temperature, but confidence in forecasts of precipitation and atmospheric circulation is much lower. Recent advances in seasonal and annual prediction show that the signal-to-noise ratio can be too small in climate models, requiring a very large ensemble to extract the predictable signal. Here, we reassess decadal prediction skill using a much larger ensemble than previously available, and reveal significant skill for precipitation over land and atmospheric circulation, in addition to surface temperature. We further propose a more powerful approach than used previously to evaluate the benefit of initialisation with observations, improving our understanding of the sources of skill. Our results show that decadal climate is more predictable than previously thought and will aid society to prepare for, and adapt to, ongoing climate variability and change. Forecasting: Large ensemble improves decadal climate predictions There is increasing demand for near-time climate predictions to provide guidance for adaptation planning at policy-relevant timescales. Although previous work has shown some skill in forecasting decadal surface temperature, it has proven more difficult to make predictions for precipitation and atmospheric circulation. By using a large, multi-model ensemble of climate models, a multi-institution team lead by Doug Smith of the Met Office Hadley Centre, UK were able to make skillful decadal predictions for near surface temperature, precipitation for the Sahel and broad swathes of Europe and Eurasia, and mean sea level pressure for many regions, with some exceptions being predictions for the South Atlantic and Southern Ocean. Further work is needed to understand whether the instances in which forecasts and observations differ are due to internal variability or external factors such as solar variability, volcanoes and anthropogenic aerosols.