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In spite of the chaotic nature of the atmosphere and involvement of complex nonlinear dynamics, forecasting climate fluctuations over different timescales is feasible due to the interaction between the atmosphere and the slowly varying underlying surfaces. This review provides insights into climate predictions across subseasonal to decadal timescales and into making projections of future climate change. Different sources of uncertainty in climate predictions are discussed, including internal variability uncertainty, which is large for short-term predictions of up to a decade or two, model uncertainty for predictions at all timescales, and scenario uncertainty for climate change projections at the end of this century. Climate models have been significantly improved in recent decades, mostly through improved parameterization of unresolved processes and enhancement of the spatial resolution, while ensemble forecasting has also been developed to capture strong predictable signals. Future research should aim to reduce uncertainty in climate predictions, for example, through the application of high-resolution climate models. However, sub-grid-scale features would still be parameterized, underlining the need for further improvements in physical parameterizations to account for sub-grid-scale processes. There is also a need for improvement and extension of the current observing system, which will greatly advance understanding of the key processes and features in the climate system. The advanced observing system in the future will also be beneficial for more accurate representation of the initial state of the components of the climate system in order to obtain more accurate climate predictions. In spite of progress in model development, the spread of projected precipitation by different models under a specific radiative forcing of greenhouse gases is still large at the regional scale. Improving future projections of regional precipitation requires better accounting for internal variability and model uncertainty, which can be partly achieved by improvement and extension of the observing system.

Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill. Plain Language Summary Understanding how the climate will look in one to 10 years is useful for many industries, but this task is very difficult. One method for making forecasts on these timescales is called analog forecasting. In analog forecasting, a researcher finds past states in observations, or states in a climate model simulation, that look like the current state of the climate, and uses how those maps changed over time to predict how the climate will change over time. Some regions are more important for determining how a climate state will change over time, and we use a machine learning method called a neural network to identify these important regions. We find that if we only look at these important regions when determining if two climate states are similar or not, we can improve our analog forecasting skill. Key Points An interpretable neural network provides a spatially‐weighted mask for selecting optimal analogs Analogs selected with the weighted mask offer more skillful forecasts than traditional methods for selecting analogs The learned mask highlights precursor regions for predicting large‐scale climate anomalies in a perfect model framework

The capability to anticipate the exceptionally rapid warming of the Northwest Atlantic Shelf and its evolution over the next decade could enable effective mitigation for coastal communities and marine resources. However, global climate models have struggled to accurately predict this warming due to limited resolution; and past regional downscaling efforts focused on multi‐decadal projections, neglecting predictive skill associated with internal variability. We address these gaps with a high resolution (1/12°) ensemble of dynamically downscaled decadal predictions. The downscaled simulations accurately predicted past oceanic variability at scales relevant to marine resource management, with skill typically exceeding global coarse‐resolution predictions. Over the long term, warming of the Shelf is projected to continue; however, we forecast a temporary warming pause in the next decade. This predicted pause is attributed to internal variability associated with a transient, moderate strengthening of the Atlantic meridional overturning circulation and a southward shift of the Gulf Stream. Plain Language Summary The Northwest Atlantic Shelf is experiencing a rapid rise in ocean temperatures, one of the fastest globally, impacting the region's marine resources. Global coupled models struggle to accurately simulate this regional warming and have large uncertainties associated with the future evolution of this warming. To address this issue, we developed a high‐resolution decadal prediction system for the Northwest Atlantic Shelf which downscales global decadal predictions using a high‐resolution regional ocean model. The downscaled simulations accurately predict past oceanic variability and forecast a temporary pause in warming over the next decade due to natural changes in the Atlantic meridional overturning circulation and the position of the Gulf Stream. This predictive capability could pave the way for the implementation of effective mitigation strategies, benefiting coastal communities and marine resources alike. Key Points Dynamical downscaling enhances decadal prediction skill of Northwest Atlantic Shelf sea surface temperatures A temporary respite from rapid Northwest Atlantic Shelf warming is forecast for the coming decade The forecast warming pause is attributed to a modest strengthening of the Atlantic meridional overturning circulation

The Northwest Pacific (NWP) tropical cyclone (TC) activity exhibits significant decadal variations with alternating active and inactive periods. However, it remains unknown whether such kinds of decadal variations are predictable. Here, we develop a dynamic-statistic model for the decadal predictions of the tropical cyclone genesis frequency (TCGF) and accumulated cyclone energy (ACE) index of the NWP TCs. The dynamic-statistic model is a combination of decadal prediction experiments by coupled general circulation models (CGCM) from the CMIP6 Decadal Climate Prediction Project (DCPP) and multiple linear regression models based on the correlation relationships between the NWP TCGF (ACE) and large-scale variability modes of sea surface temperature (SST) anomalies in observations. For the TCGF, we first calculate anomalous SST intensities associated with Atlantic multidecadal variability (AMV), Pacific decadal oscillation (PDO), and global mean SST (GMSST) predicted by the decadal prediction experiments. Then, they are substituted into the regression model trained by the historical observational TCs and SST to predict the NWP TCGF. For the ACE, one more predictor, viz. the anomalous SST in the NWP, is involved in its regression model. The dynamic-statistic model can be applied for both deterministic and probabilistic predictions with multi-model ensemble mean, and individual members of the decadal prediction experiments used, respectively. Retrospective predictions for the past 50 years show that the correlation skill of the deterministic predictions for the NWP TCGF (ACE) in the future 2–5 and 6–9 years reach 0.71 and 0.59 (0.59 and 0.41), respectively. The results of this dynamic-statistic model will provide decision-makers of the western Pacific Rim countries with valuable information to adapt to variations in NWP TC activity over the next 10 years.

There are two main approaches for dealing with model biases in forecasts made with initialized climate models. In full-field initialization, model biases are removed during the assimilation process by constraining the model to be close to observations. Forecasts drift back towards the model’s preferred state, thereby re-establishing biases which are then removed with an a posterior lead-time dependent correction diagnosed from a set of historical tests (hindcasts). In anomaly initialization, the model is constrained by observed anomalies and deviates from its preferred climatology only by the observed variability. In theory, the forecasts do not drift, and biases may be removed based on the difference between observations and independent model simulations of a given period. Both approaches are currently in use, but their relative merits are unclear. Here we compare the skill of each approach in comprehensive decadal hindcasts starting each year from 1960 to 2009, made using the Met Office decadal prediction system. Both approaches are more skilful than climatology in most regions for temperature and some regions for precipitation. On seasonal timescales, full-field initialized hindcasts of regional temperature and precipitation are significantly more skilful on average than anomaly initialized hindcasts. Teleconnections associated with the El Niño Southern Oscillation are stronger with the full-field approach, providing a physical basis for the improved precipitation skill. Differences in skill on multi-year timescales are generally not significant. However, anomaly initialization provides a better estimate of forecast skill from a limited hindcast set.

We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960–2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. Further improvements of the simulated mean state of ICON-ESM, e.g. through fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean, are expected to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.

The occurrence of extreme climate events in the coming years is modulated by both global warming and internal climate variability. Anticipating changes in frequency and intensity of such events in advance may help minimize the impact on climate-vulnerable sectors and society. Decadal climate predictions have been developed as a source of climate information relevant for decision-making at multi-annual timescales. We evaluate the multi-model forecast quality of the CMIP6 decadal hindcasts in predicting a set of indices measuring different characteristics of temperature and precipitation extremes for the forecast years 1-5. The multi-model ensemble skillfully predicts the temperature extremes over most land regions, while the skill is more limited for precipitation extremes. We further compare the prediction skill for these extreme indices to the skill for mean temperature and precipitation, finding that the extreme indices are predicted with lower skill, particularly those representing the most extreme days. We find only small and region-dependent improvements from model initialization in comparison to historical forcing simulations. This systematic evaluation of decadal hindcasts is essential when providing a climate service based on decadal predictions so that the user is informed on the trustworthiness of the forecasts for each specific region and extreme event.

Global mean near surface temperature change is the key metric by which our warming climate is monitored and for which international climate policy is set. At the end of each year the Met Office issues a global mean temperature forecast for the coming year. Following on from the new record in 2023, we predict that 2024 will likely (76% chance) be a new record year with a 1‐in‐3 chance of exceeding 1.5°C above pre‐industrial. Whilst a one‐year temporary exceedance of 1.5°C would not constitute a breach of the Paris Agreement target, our forecast highlights how close we are now to this. Our 2024 forecast is primarily driven by the strong warming trend of +0.2°C/decade (1981–2023) and secondly by the lagged warming effect of a strong tropical Pacific El Niño event. We highlight that 2023 itself was significantly warmer than the Met Office DePreSys3 forecast, with much of this additional observed warming coming from the southern hemisphere, the cause of which requires further understanding. Predictions of global mean surface temperature for 2024 are made showing for the first time a significant chance of exceeding 1.5°C above pre‐industrial. Whilst a one‐year temporary exceedance of 1.5°C would not be a breach of the Paris Agreement target, our forecast highlights how close we are now to this. This further motivates efforts to rapidly transition to net zero global emissions of greenhouse gases and to undertake research to better understand the recent jump in global temperature.

A fully-coupled general circulation model of intermediate complexity is documented. The study presents an overview of the model climatology and variability, with particular attention to the phenomenology of processes that are relevant for the predictability of the climate system on seasonal-to-decadal time-scales. It is shown that the model can realistically simulate the general circulation of the atmosphere and the ocean, as well as the major modes of climate variability on the examined time-scales: e.g. El Niño-Southern Oscillation, North Atlantic Oscillation, Tropical Atlantic Variability, Pacific Decadal Variability, Atlantic Multi-decadal Variability. Potential applications of the model are discussed, with emphasis on the possibility of generating sets of low-cost large-ensemble retrospective forecasts. We argue that the presented model is suitable to be employed in traditional and innovative model experiments that can play a significant role in future developments of seasonal-to-decadal climate prediction.

The increasing demand for climate information that spans seasonal to multi-annual time scales poses a challenge for current prediction systems, which are traditionally designed for specific forecast horizons. This study addresses this gap by proposing a new method to generate seamless climate information from seasonal to decadal time scales. We develop a constraining approach based on ensemble member selection, in which decadal prediction members are selected to match the seasonal forecast ensemble mean of sea surface temperature. The method leverages the higher skill of seasonal predictions in capturing interannual climate variability, particularly El Niño–southern oscillation, to constrain decadal forecasts using the most recent climate information. Results show that the method to constrain decadal predictions improves the forecast skill over the Niño3.4 region up to 12 months and enhances the near-surface temperature predictions over broad parts of the globe, with modest improvements in precipitation. This work highlights the practical potential of combining seasonal and decadal prediction systems and offers a first step toward operational, seamless climate services across monthly to multi-year timescales.