Multi-annual predictions of hot, dry and hot-dry compound extremes

Toward usable predictive climate information at decadal timescales

Germany's National Meteorological Service, Deutscher Wetterdienst (DWD), is working on an operational seamless climate prediction approach: What started in 2016 with operational seasonal climate predictions, was later complemented with decadal climate predictions. Since 2022, DWD publishes decadal, seasonal, and subseasonal climate predictions on one single, comprehensive climate prediction website www.dwd.de/climatepredictions [1].While global simulations of decadal and seasonal predictions are produced by DWD’s climate prediction systems, global subseasonal predictions are retrieved from the European Centre of Medium-Range Weather Forecast (ECMWF). The next step in the operational processing chain is the empirical-statistical downscaling EPISODES [2], which results in high-resolution climate predictions (approx. 5 km) for Germany.Both global and regional climate predictions are evaluated using the Meteorological Analyzation and Visualization System MAVIS, a fork of the FREVA system (Free Evaluation System Framework for Earth System Modeling) [3]. We evaluate ensemble mean predictions using the Mean Squared Error Skill Score (MSESS) and the Pearson Correlation Coefficient. Probabilistic climate predictions are evaluated using the Ranked Probability Skill Score (RPSS).Ensemble mean and probabilistic climate prediction results of global and downscaled simulations, as well as the evaluation results are jointly published on DWD’s climate prediction website. The user-friendly graphical presentation is consistent for all displayed regions (global, Europe, Germany, and German cities) and across all time scales and was developed as a co-design between DWD and various national users.We work on several extensions of the website: multi-year seasonal predictions (e.g. 5-year summer means), the prediction of drought indices and El Nino Southern Oscillation predictions. In addition, a seamless time series combining observations, climate predictions and climate projections is in preparation. [1] A. Paxian, B. Mannig, M. Tivig, K. Reinhardt, K. Isensee, A. Pasternack, A. Hoff, K. Pankatz, S. Buchholz, S. Wehring, P. Lorenz, K. Fröhlich, F. Kreienkamp, B. Früh (2023). The DWD climate predictions website: towards a seamless outlook based on subseasonal, seasonal and decadal predictions. Manuscript in review.[2] Kreienkamp, F., Paxian, A., Früh, B., Lorenz, P., Matulla, C., 2018. Evaluation of the Empirical-Statistical Downscaling method EPISODES. Clim. Dyn. 52, 991–1026 (2019). https://doi.org/10.1007/s00382-018-4276-2.[3] Kadow, C., Illing, S., Lucio-Eceiza, E.E., Bergemann, M., Ramadoss, M., Sommer, P.S., Kunst, O., Schartner, T., Pankatz, K., Grieger, J., Schuster, M., Richling, A., Thiemann, H., Kirchner, I., Rust, H.W., Ludwig, T., Cubasch, U. and Ulbrich, U., 2021. Introduction to Freva – A Free Evaluation System Framework for Earth System Modeling. Journal of Open Research Software, 9(1), p.13. DOI: http://doi.org/10.5334/jors.253

Both global warming and internal climate variability modulate changes in the intensity and frequency of extreme climate events. Anticipating such variations years in advance may help minimise the impact on climate-vulnerable sectors and society, as well as enable short-term adaptation strategies and early-warning systems in a changing climate. Decadal climate predictions are a source of climate information for multi-annual timescales. They are provided by climate forecast systems similar to the models used for long-term climate projections but that have been initialised with the best estimate of the contemporaneous conditions of the climate system. However, before using the predictions, the forecast quality should be assessed. This is an essential step to evaluate the accuracy of the predictions and find windows of opportunity (variables/indices, regions and forecast times) to provide climate services with data of sufficient quality to satisfy the user requirements.We evaluate the deterministic and probabilistic forecast quality of the multi-model ensemble built with all the available decadal hindcasts (i.e., retrospective decadal predictions) contributing to CMIP6, which consists of a total of 133 ensemble members from 13 forecast systems. The forecast quality assessment has been performed for predictions of seasonal and annual indices of daily temperature and precipitation extremes for the forecast years 1-5. These indices measure the intensity and frequency of hot and cold temperature extremes, and the intensity and rainfall accumulation related to heavy precipitation extremes. The prediction skill for the temperature and precipitation extreme indices is further compared to the skill for mean temperature and precipitation, respectively. In order to assess the impact of the model initialisation, the predictions are compared against historical forcing simulations (i.e., retrospective climate projections) created with the same models, consisting of a total of 134 ensemble members from the same forecast systems as the decadal hindcasts.We find that the decadal hindcasts skillfully predict both mean and extreme temperature indices over most of the globe for multi-annual periods. The forecast quality for mean precipitation and extreme precipitation indices is generally low, and significant skill is found only over some limited regions. The reduced quality of the precipitation predictions with respect to temperature is due to the relatively smaller effect of human-induced warming for this variable. The comparison between the skill for mean variables and extreme indices shows that the extreme indices are generally predicted with lower skill, especially those related to the intensity of extreme events. We find generally small and region-dependent improvements from model initialisation compared to historical forcing simulations. The added value due to initialisation is higher for the mean variables than for the extreme indices. Besides, such skill differences differ between indices, especially those representing extreme temperature. This systematic evaluation of decadal hindcasts is essential when providing a climate service based on decadal predictions so that the user is informed about the trustworthiness of the forecasts for each specific region and extreme event. Also, comparing decadal hindcast and historical simulations may help climate services providers select the highest-quality information from these different data sources.

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.

Decadal climate predictions are a source of climate information to anticipate the evolution of the climate system from 1 to 10 years ahead. Whereas both climate projections and decadal predictions contain information about external forcings, their main difference is that decadal predictions also include information on the phase of the internal climate variability. To achieve this, decadal climate models are initialised once per year with observation-based initial conditions.  This study assesses the effectiveness of various statistical downscaling methods applied to multi-model decadal predictions of mean near-surface air temperature and precipitation for the forecast years 1-5 over the Iberian Peninsula. The multi-model ensemble combines predictions from 13 forecast systems contributing to the Decadal Climate Prediction Project (DCPP) component of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The performance of the different downscaling methods is determined by comparing their forecast quality against the raw, coarse-resolution predictions using four deterministic or probabilistic metrics: the Anomaly Correlation Coefficient (ACC), Root Mean Square error Skill Score (RMSSS), Ranked Probability Skill Score (RPSS) and Continuous Ranked Probability Skill Score (CRPSS). The downscaling and forecast quality assessment are carried out using the high-resolution ERA5Land reanalysis as the reference dataset, and it is performed in leave-one-out cross-validation mode in order to emulate real-time conditions and not to overestimate the actual skill. Three kinds of downscaling methods have been examined. The first type is  based on calibrating the interpolated raw predictions (i.e. correcting biases in the mean value or variance, among others). The second involves building linear regressions using different predictors: (i) large-scale decadal indices as (e.g. the Atlantic Multi-decadal Variability, AMV; or the North Atlantic Oscillation, NAO) (ii) interpolated model data (basic linear regression) or (iii) a combination of the 9 nearest neighbours of model data. Finally, the third approach involves the search for past analog days in the high-resolution reference dataset. The results show that the skill estimates primarily depend on the calibration or linear regression approaches, with small differences from the interpolation method used during the downscaling. While the first type of methods maintains the spatial distribution of the skill compared to the raw predictions, the second and third types can change it. For temperature, the raw predictions show high skill, which is maintained after applying calibration, basic linear regressions or 9 nearest neighbours linear regression. However, the skill is reduced after calculating the linear regressions with external predictors. For precipitation, the skill of the raw predictions is rather low, and the calibration methods do not generally increase such skill. On the other hand, the linear regression method using the AMV index as a predictor is the one that shows the most improvement in skill compared to the raw predictions in some regions.

The High Performance Computing System (HPC) CRAY XE6 operated by HLRS is a powerful tool to study various aspects of the regional climate. Employing the regional climate model (RCM) COSMO-CLM, the research focus of IMK-TRO is on the regional atmospheric water cycle, especially on extremes, and different goals are pursued in individual research projects (MiKlip, KLIMOPASS and KLIWA). The simulation regions comprise Germany, Europe, and Africa with resolutions varying from 50 km to 2.8 km. Furthermore, different time spans are investigated. Decadal simulations are performed to assess decadal regional climate predictability. Projections of the future climate consider periods up to the end of the twenty-first century. To quantify the uncertainty of climate projections and predictions as well as the quality of the models, ensembles are built by different techniques. The Soil-Vegetion-Atmosphere-Transfer model (SVAT) VEG3D is coupled to COSMO-CLM via OASIS3-MCT to investigate the effect of soil and vegetation processes on decadal climate predictions. High resolution (2.8 km) experiments are performed for the State of Baden–Wurttemberg to study the potential added value and extremes. Computational capacities from 100 to 650 node–hours per simulated year (Wall Clock Time) are required for these simulations.

Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.

A cross-validated model output statistics (MOS) approach is applied to precipitation data from the high-resolution regional climate model CCLM for Europe. The aim is to remove systematic errors of simulated precipitation in decadal climate predictions. We developed a two-step bias-adjustment approach. In step one, we estimate model errors based on a long-term ‘CCLM assimilation run’ (regionalizing data from a global assimilation run) and observational data. In step two, the resulting transfer function is applied to the complete set of decadal hindcast simulations (285 individual runs). In contrast to lead-time-dependent bias-adjustment approaches, this one is designed for variables with poor decadal prediction skill and without dominant lead-time-dependent bias. In terms of the CCLM assimilation run, MOS is shown to be effective in predictor selection, model skill improvement, and model bias reduction. Yet, the positive effect of MOS correction is accompanied with an underestimation of precipitation variability. After MOS application, an estimated mean square skill score of more than 0.5 is observed regionally. Simulated precipitation in decadal hindcasts is further improved when the MOS is trained on the basis of other decadal hindcasts from the same regional climate model but with a large underestimation in forecast uncertainty. Our results suggest that the MOS system derived from the assimilation run is less effective but allows the potential climate predictability in decadal hindcasts and forecasts to be retained. Using hindcasts itself for training is recommended unless a statistical method is capable of distinguishing biases and predictions within a hindcasts dataset.

The DWD climate predictions website www.dwd.de/climatepredictions presents different climate predictions on a common website to support decision-making processes on different time scales: post-processed subseasonal prediction products derived from the IFS forecasts provided by ECMWF for the coming weeks and operational seasonal and decadal predictions of the German climate prediction system for the coming months and years. The user-oriented evaluation and design of this climate service was developed in cooperation with users from various sectors (e.g. water, energy, agriculture or forestry) and is consistent across all time scales. The website offers maps, time series and tables of ensemble mean and probabilistic predictions in combination with their skill. The products are displayed for weekly, 3-month as well as 1- and 5-year temperature means and precipitation sums for different regions (world, Europe, Germany, German regions). For Germany, the statistical downscaling EPISODES is used to reach high spatial resolution required by several German climate data users. A lead-time dependent bias correction is applied, and decadal predictions are recalibrated to improve drift and ensemble spread. We use the MSESS and RPSS to evaluate the skill of climate predictions compared to reference predictions (applied by users as an alternative to climate predictions), e.g. the ‘observed climatology’ or ‘uninitialized climate projections’. The significance is tested at a 5% level. Different layers of complexity are presented: Within the ‘basic climate predictions’ section, a traffic light indicates if regional-mean predictions are significantly better (green), not significantly different (yellow) or significantly worse (red) than a reference prediction. Within the ‘expert climate predictions’ section, prediction maps show per grid box the prediction (via the color of dots) and its skill (via the size of dots). The co-development of this climate service with users from different sectors improves its comprehensibility and usability. Outlooks are regularly communicated, and user feedback loops are continuously performed to evaluate and improve this climate service. Drought-related variables and indices such as the climatic water balance (precipitation minus potential evapotranspiration), SPI (standardized precipitation index) or SPEI (standardized precipitation evaporation index) are evaluated and will soon be included in the climate outlook. Cooperation work is ongoing to use climate predictions to forecast soil moisture and groundwater levels. Plans for future extensions of this climate service include multi-year seasonal predictions, multi-model predictions, large-scale and extreme indices (e.g. for ENSO, NAO, heat), and tools to further clarify the presented information (e.g. video clips or interactivity). Finally, we work on a time series combining observations, subseasonal, seasonal and decadal climate predictions and climate projections, the next step towards a seamless climate service for Germany.

<p>Here we present an overview of results emerging from a project to develop prototype decadal climate prediction services, funded by the EU Copernicus Climate Change Service (C3S). The field of interannual to decadal climate prediction has matured rapidly over the last ~15 years, becoming an established part of the Coupled Model Intercomparison Project (CMIP) process with multi-model decadal climate predictions made in CMIP5 and CMIP6 (DCPP MIP). It has further been highlighted by the recent creation of the WMO Lead Centre for Annual-to-Decadal Climate Prediction. Whilst these activities have led to rapid development in our understanding of decadal climate predictability and mechanisms driving global and regional annual to decadal climate variability, the creation of useful climate services on this timescale is still in its infancy.</p><p>This EU funded project was designed to start to address decadal climate services and brings together many of the key European institutions involved in decadal climate predictions from four different countries: Germany (DWD), Italy (CMCC), Spain (BSC) and the UK (Met Office). Each partner is working with a different sector: infrastructure, energy, agriculture and insurance where they have been developing a prototype decadal climate service in partnership with a user in that sector. Here we report on the progress made so far and highlight a number of key lessons learned along the way. These include the use of both large multi-model ensembles and more predictable large-scale circulation indicators in order to give skilful regional predictions of user relevant variables. We also describe the development of a common product format to present forecast information to users, this contains essential information about the current probabilistic forecast, retrospective forecast skill and reliability.</p>

This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.

Decadal climate predictability in the North Atlantic is largely related to ocean low frequency variability, whose sensitivity to initial conditions is not very well understood. Recently, three-dimensional oceanic temperature anomalies optimally perturbing the North Atlantic Mean Temperature (NAMT) have been computed via an optimization procedure using a linear adjoint to a realistic ocean general circulation model. The spatial pattern of the identified perturbations, localized in the North Atlantic, has the largest magnitude between 1000 and 4000 m depth. In the present study, the impacts of these perturbations on NAMT, on the Atlantic meridional overturning circulation (AMOC), and on climate in general are investigated in a global coupled model that uses the same ocean model as was used to compute the three-dimensional optimal perturbations. In the coupled model, these perturbations induce AMOC and NAMT anomalies peaking after 5 and 10 years, respectively, generally consistent with the ocean-only linear predictions. To further understand their impact, their magnitude was varied in a broad range. For initial perturbations with a magnitude comparable to the internal variability of the coupled model, the model response exhibits a strong signature in sea surface temperature and precipitation over North America and the Sahel region. The existence and impacts of these ocean perturbations have important implications for decadal prediction: they can be seen either as a source of predictability or uncertainty, depending on whether the current observing system can detect them or not. In fact, comparing the magnitude of the imposed perturbations with the uncertainty of available ocean observations such as Argo data or ocean state estimates suggests that only the largest perturbations used in this study could be detectable. This highlights the importance for decadal climate prediction of accurate ocean density initialisation in the North Atlantic at intermediate and greater depths.

Decadal climate prediction is a branch of climate modelling with the theoretical potential to anticipate climate impacts years in advance. Here we present analysis of the ENSEMBLES decadal simulations, the first multi-model decadal hindcasts, focusing on the skill in prediction of temperature and precipitation—important for impact prediction. Whilst previous work on this dataset has focused on the skill in multi-year averages, we focus here on the skill in prediction at smaller timescales. Considering annual and seasonal averages, we look at correlations, potential predictability and multi-year trend correlations. The results suggest that the prediction skill for temperature comes from the long-term trend, and that precipitation predictions are not skilful. The potential predictability of the models is higher for annual than for seasonal means and is largest over the tropics, though it is low everywhere else and is much lower for precipitation than for temperature. The globally averaged temperature trend correlation is significant at the 99% level for all models and is higher for annual than for seasonal averages; however, for smaller spatial regions the skill is lower. For precipitation trends, the correlations are not skilful on either annual or seasonal scales. Whilst climate models run in decadal prediction mode may be useful by other means, the hindcasts studied here have limited predictive power on the scales at which climate impacts and the results presented suggest that they do not yet have sufficient skill to drive impact models on decadal timescales.

Decadal climate prediction, where climate models are initialized with the contemporaneous state of the Earth system and run for a decade into the future, represents a new source of near-term climate information to better inform decisions and policies across key climate-sensitive sectors. This paper illustrates the potential usefulness of such predictions for building a climate service for agricultural needs. In particular, we assess the forecast quality of multi-model climate predictions in estimating two user-relevant drought indices, Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI), at multi-annual timescales during European summer. We obtain high skill for predicting five-year average (forecast years 1–5) SPEI across Southern Europe, while for the same forecast period SPI exhibits high and significant skill over Scandinavia and its surrounding regions. In addition, an assessment of the added value of initialized decadal climate information with respect to standard uninitialized climate projections is presented. The model initialization improves the forecast skill over Central Europe, the Balkan region and Southern Scandinavia. Most of the increased skill found with initialization seems to be due to the climate forecast systems ability to improve the extended summer precipitation and potential evapotranspiration forecast, as well as their ability to adequately represent the observed effects of these climate variables on the drought indices.

<p>Decadal climate predictions are a new source of climate information for inter-annual to decadal time scales, which is of increasing interest for users. Forecast quality assessment is essential to identify windows of opportunity (e.g., variables, regions, and lead times) with skill that can be used to develop a climate service and inform users in several sectors. Also, it can help to monitor improvements in current forecast systems. The Decadal Climate Prediction Project Component A (DCPP-A) of the Coupled Model Intercom-parison Project Phase 6 (CMIP6) now provides the most comprehensive set of retrospective decadal predictions from multiple forecast systems. The increasing availability of these simulations leads to the question of how to best post-process the raw output from the forecast systems so that the most useful and reliable information is provided to users.</p><p>This work evaluates the quality of deterministic and probabilistic forecasts for spatial fields of near-surface air temperature and precipitation, and time series of the Atlantic multi-decadal variability index (AMV) and global near-surface air temperature anomalies (GSAT) generated from all the available decadal predictions contributing to CMIP6/DCPP-A (169 members from 13 forecast systems). The predictions generally show high skill in predicting temperature and the AMV and GSAT time series, while the skill is more limited for precipitation. Also, different approaches for building a multi-model forecast are compared (pooling all ensemble members versus combining the averages from individual forecast systems), finding small differences. Besides, the multi-model ensemble is compared to the individual forecast systems. The best system usually provides the highest skill. However, the multi-model ensemble is a reasonable choice for not having to select the best system for each particular variable, forecast period and region. Furthermore, the decadal predictions are compared to the uninitialized historical climate simulations (195 members from the same forecast systems as the decadal prediction members) to estimate the impact of initialization. An added value is found for temperature over several ocean and land regions, and for the AMV and GSAT time series, while it is more reduced for precipitation. Moreover, the full DCPP-A ensemble is compared to a sub-ensemble of predictions that could be provided in near real-time for a potential operational product generation. The comparison shows a benefit of using a large ensemble over several regions, especially for temperature. Finally, the implications of these results in a climate services context are discussed.</p><div> </div>