CL5.3.4
Presentations: Thu, 26 May | Room 0.14
Many sectors of society are vulnerable to decadal changes in climate, which impact food security, freshwater availability, spread of pests and diseases, heat waves, droughts, floods, cyclones, wildfires, energy supply and demand, transport, migration, and conflict. On decadal timescales climate is influenced by both internal variability and changes in radiative forcing. Climate predictions that are initialised with observations are needed to account for all of these factors and will be reviewed in this talk.
Understanding the drivers of decadal climate is crucial for gaining confidence in forecasts. One hypothesis, namely that Arctic sea ice loss weakens mid-latitude westerly winds, promoting more severe cold winters, has sparked more than a decade of scientific debate. The Polar Amplification Model Intercomparison Project was developed to address this issue and results from coordinated multi-model experiments will be presented that support the above hypothesis and suggest that this effect is underestimated by current models. However, even when accounting for this underestimation, the response to Arctic sea ice is small compared to yearly variations in mid-latitude winters.
For predictions to be useful they must be skilful and reliable. There is mounting evidence that models may underestimate the strength of predictable signals, especially for atmospheric circulation in the North Atlantic. This error has been termed the “signal-to-noise paradox” since it leads to the unexpected situation that models can predict the real world better than one of their own ensemble members. Skilful predictions can be achieved using a very large ensemble, but the model output cannot be taken at face value and needs calibrating to obtain skilful and reliable forecasts. Given the potential impacts of changes in atmospheric circulation, understanding why the signal-to-noise ratio is too small in current climate models, and assessing the extent to which correcting this model error would reduce uncertainties in regional climate change projections of the coming decades, are high priority areas for future research.
How to cite: Smith, D.: Decadal climate predictions, impacts of Arctic sea ice loss, and the signal-to-noise paradox, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2912, https://doi.org/10.5194/egusphere-egu22-2912, 2022.
Winter (DJF) 2020/21 in the North Atlantic/European sector was characterised by the negative phase of the North Atlantic Oscillation (NAO). However, this was not well forecast by the leading seasonal prediction systems. We focus on forecasts from GloSea5, which was the Met Office operational seasonal prediction system at the time. Forecasts initialised in November 2020, at the 1-month lead time, indicated that a positive NAO was likely, although a few ensemble members did agree with the eventual outcome. Analysis suggests that the sudden stratospheric warming (SSW) that occurred in early January 2021 and an active MJO in late January/early February 2021 probably contributed to the observed negative NAO. In particular, GloSea5 indicated a rather low probability for SSW activity, which may well have been exacerbated by the forecast of a stronger than observed La Niña by this system.
How to cite: Ineson, S., Lockwood, J., Stringer, N., Thornton, H., and Scaife, A.: Predictability of European Winter 2020/21, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9713, https://doi.org/10.5194/egusphere-egu22-9713, 2022.
During the 2019/2020 winter season the extremely high air temperature and precipitation were recorded over northern Eurasia, eastern Asia and eastern North America. Over the UK this winter was stormy and one of the wettest for the entire observational period. Moreover, it was the only winter without stable snow cover in the central East European Plain. The reason of such exceptional weather is the domination of a North Atlantic Oscillation (NAO) positive phase during the whole season.
Since the NAO effect on winter weather is strong, prediction of its phase is a challenge for all national meteorological services. Several of them predicted the positive sign of North Atlantic Oscillation phase for 2019/2020 winter season, but underestimated the magnitude and duration. Forecasts obtained from INM RAS climate model (INMCM5) demonstrated consistent results for the considered season.
In this work we use the INMCM5 to study the sources of the predictability of both the extremely positive NAO phase and the weather fields anomalies in the 2019/2020 winter season. In particular we consider whether the INM RAS climate model simulates the positive Indian Ocean dipole — positive North Atlantic Oscillation phase teleconnection.
The research was carried out during the student educational program "Computational Technologies, Higher Order Data Analysis and Modelling" at the Sirius University and partially supported by the Russian Science Foundation (project 20‑17‑00190).
How to cite: Tarasevich, M., Vorobyeva, V., Chernenkov, A., Gasanov, M., Bardashov, D., and Volodin, E.: Influence of the ocean initial state on the weather anomalies simulation for 2019/2020 winter in the INMCM5 seasonal hindcasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-599, https://doi.org/10.5194/egusphere-egu22-599, 2022.
We investigate the potential for enhancing the seasonal prediction skill of mid-latitude cyclonic activity, represented by eddy kinetic energy (EKE) at 250 hPa over the North Atlantic and Europe, in hindcast simulations with the Max Planck Institute Earth System Model (MPI-ESM) against the ECMWF ERA5 reanalysis. Our analysis focuses on wintertime months (December-March) from 1982 to 2019, with a 30-member seasonal hindcast ensemble initialized every November 1st. Based on the initial confirmation that in both ERA5 reanalysis and MPI-ESM hindcasts, the eddy-driven jet stream and the wintertime North Atlantic Oscillation (NAO) play a significant role in wintertime's spatial and temporal distribution of mid-latitude cyclonic activity, we perform ensemble subsampling.
Specifically, we sample each winter so that a northern position of the jet stream is consistent with a positive phase of the NAO and represents poleward enhanced EKE activity. In contrast, a southern position of the jet stream is consistent with a negative phase of the NAO and represents equatorward enhanced EKE activity. Preliminary analysis of the predictive skill of MPI-ESM hindcasts with respect to ERA5 shows that such subsampling with respect to a consistent representation of the jet stream position and the NAO phase leads to improvements over the skill from the 30-member ensemble mean, with significant correlations concentrated over areas of major frequency of storm tracks. Our results put into practical use that an enhanced representation of the large-scale climate variability plays a crucial role in the long-term prediction of high-frequency events such as mid-latitude cyclones.
How to cite: Aranyossy, A., Brune, S., Hellmich, L., Dobrynin, M., Krieger, D., and Baehr, J.: Seasonal Predictability of wintertime North Atlantic cyclonic activity through the NAO and the eddy-driven jet stream, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3948, https://doi.org/10.5194/egusphere-egu22-3948, 2022.
The Indian Ocean Dipole (IOD) is a major source of seasonal climate variability in the
Indian Ocean. This dipole has major impacts on the Indian Ocean region and through
teleconnections can influence the seasonal climate of remote regions as well. In late 2019 a
major IOD event contributed to a strong positive North Atlantic Oscillation (NAO) of that
winter. Thus, a good understanding of the mechanism that transports information from
the Indian Ocean to the North Atlantic is desirable. In this contribution we investigate
the special teleconnection of the winter of 2019 and analyse the transport mechanism.
In model experiments with the OpenIFS from ECMWF we show that the NAO in the
winter 2019 is influenced by the Indian Ocean Dipole. We use hindcast ensemble model
experiments to analyse the behaviour of the IOD and its impact on the NAO. These
seasonal hindcast experiments are started from the 01. November 2019 and run for the
DJF season 2019/2020. Since the OpenIFS is uncoupled we change the Sea Surface
Temperature (SST) boundary conditions in regions of importance to the NAO (like the
ENSO region, the North Atlantic, and also the Indian Ocean). With these perturbations
we identify the relative importance of individual ocean regions to the state of the NAO
in the winter of 2019.
We contrast the experiments with the perturbed SST conditions to a control forecast and
ERA5 reanalysis. We find that removing the IOD has a significant impact on the NAO of
the 2019/2020 DJF season, pushing the NAO to a more negative state. Additionally the
contrast between control forecast and model experiments shows Rossby Waves emanating
from the Indian Ocean over the North Pacific and the Arabian Peninsular.
Experiments with perturbations in other ocean regions show that some signals, like ENSO,
can suppress the impact of the IOD on the NAO, but in their absence the positive IOD
event of 2019 did likely contribute to the strong positive NAO of 2019/2020.
How to cite: Hempel, T., Weisheimer, A., and Palmer, T.: The seasonal teleconnections of the Indian Ocean Dipole to the North Atlantic region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7631, https://doi.org/10.5194/egusphere-egu22-7631, 2022.
Since the 1980s, external forcing from increasing greenhouse gases and declining aerosols has had a large effect on European summer temperatures. The forcing therefore provides an important source of predictive skill, even for timescales as short as seasonal forecasts. However, the relative importance of forcing for seasonal forecasts has thus far not been quantified, particularly for skill on regional scales. In this study, we investigate forcing-induced skill by comparing the skill of the operational multi-model ensemble of seasonal predictions from the Copernicus climate change service (C3S) archive to that of an uninitialized ensemble of CMIP6 projections for European summers for the period 1993-2016.
We show that for some regions, such as northern Europe, the forced trend provides the primary source of 2m temperature skill in current seasonal forecast models at 2-4 month lead-times. Over some parts of northern Europe, summer correlation skill is actually higher in uninitialized predictions and in runs with long lead-times than at short lead-times suggesting that there may be problems with the initialization. Conversely, 2m temperature in the Mediterranean region is generally well predicted by seasonal forecast models out to 4-6 months due to a combination of dynamical skill and a strong forced trend.
We argue that the strong warming trends mean that even uninitialized predictions contain useful information for seasonal forecasts of European summer temperatures. However, the ability of current models to capture summer circulation patterns requires further investigation as it is still unclear whether the models are deficient in this regard or whether the summer is inherently unpredictable.
How to cite: Patterson, M., Weisheimer, A., Befort, D., and O'Reilly, C.: The strong role of external forcing in seasonal forecasts of European summer temperatures, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1793, https://doi.org/10.5194/egusphere-egu22-1793, 2022.
Slow-varying atmospheric boundaries are the main sources of seasonal climate predictions, and their footprints on climate variables may be captured as predictable patterns. Based on 36-yr hindcasts from the fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5), the most predictable patterns of the wintertime 2-m air temperature (T2m) in the extratropical Northern Hemisphere are extracted via the maximum signal-to-noise (MSN) empirical orthogonal function (EOF) analysis, and their associated predictability sources are identified. The main findings of this study are as following:
- The MSN EOF1 captures the warming trend that amplifies over the Arctic but misses the associated warm Arctic–cold continent pattern. The MSN EOF2 delineates a wavelike T2m pattern over the Pacific–North America region, which is rooted in the tropical forcing of the eastern Pacific-type El Niño–Southern Oscillation (ENSO). The MSN EOF3 shows a wavelike T2m pattern over the Pacific–North America region, which has an approximately 90° phase difference from that associated with MSN EOF2, and a loading center over midlatitude Eurasia. Its sources of predictability include the central Pacific-type ENSO and Eurasian snow cover. The MSN EOF4 reflects T2m variability surrounding the Tibetan Plateau, which is plausibly linked to the remote forcing of the Arctic sea ice.
- The information on the leading predictable patterns and their sources of predictability is further used to develop a calibration scheme to improve the prediction skill of T2m. The calibrated prediction skill in terms of the anomaly correlation coefficient improves significantly over midlatitude Eurasia in a leave-one-out cross-validation, implying a possible way to improve the wintertime T2m prediction in the SEAS5.
How to cite: Fan, H., Wang, L., Zhang, Y., Tang, Y., Duan, W., and Wang, L.: Predictable Patterns of Wintertime Surface Air Temperature in Northern Hemisphere and Their Predictability Sources in the SEAS5, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1507, https://doi.org/10.5194/egusphere-egu22-1507, 2022.
Seasonal forecasts of marine variables are not used nor validated to the same level that atmospheric variables are, despite their great potential for the planning of maritime activities. Ocean heat content (OHC) anomalies, for example, typically persist for several months, making this variable a vital component of seasonal predictability in both the ocean and the atmosphere. However, the ability of seasonal forecasting systems to predict OHC remains largely untested. Here, we present a global assessment of OHC predictability in two state-of-the-art and fully-coupled seasonal forecasting systems. Overall, we find that dynamical systems make skilful seasonal predictions of OHC in the upper 300m across a range of forecast start times, seasons and dynamical environments. Predictions of OHC are typically as skilful as predictions of sea surface temperature (SST), providing further proof that accurate representation of subsurface heat contributes to accurate surface predictions. We also compare dynamical systems to a simple anomaly persistence model to identify where dynamical systems provide added value over cheaper forecasts; this largely occurs in the equatorial regions and the tropics, and to a greater extent in the latter part of the forecast period. Regions where system performance is inadequate include the sub-polar regions and areas dominated by sharp fronts, which should be the focus of future improvements of climate forecasting systems.
Lastly, we describe efforts to encourage the use of marine variables in operational seasonal forecasting, as part of the European Union Horizon 2020 EuroSea project. We present encouraging results on the predictability of marine heat waves using OHC, which marks the first step of our strategy to provide forecasts of stakeholder-defined indicators.
How to cite: McAdam, R., Masina, S., Balmaseda, M. A., Gualdi, S., Senan, R., and Mayer, M.: Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13219, https://doi.org/10.5194/egusphere-egu22-13219, 2022.
Heat waves are among the natural hazards with the greatest social, environmental and economic impact in Mediterranean Europe. In this scenario of changing climate towards warmer conditions, heat waves are expected to increase their length and intensity during the next decades. Thus, reliable near-term forecasting for heat waves plays a fundamental role in the development of effective mitigation and adaptation strategies in these regions.
This study evaluates the prediction skill of heat waves in the Iberian Peninsula (IP) with a collection of global decadal experiments dynamically downscaled by using the Weather Research and Forecasting (WRF) model. The Decadal Prediction Large Ensemble (DPLE) has been used to set the initial and boundary conditions in the downscaling simulations. The DPLE encompasses a set of decadal experiments initialised every year from 1954 to 2015 carried out for an ensemble of 40 members with the Community Earth System Model (CESM) at NCAR. In this assessment, the decadal experiments starting in the years from 1987 to 1999 have been regionalised for 3 members of the ensemble. The downscaling simulations have been conducted in one-way mode and considering two nested domains: the EUROCORDEX domain, with resolution around 50 km, and another covering the IP at 10 km resolution, approximately.
Two indices have been used to quantify the intensity and duration of the heat waves: the Heat Wave Magnitude Index daily (HWMId) and the Warm Spell Duration Index (WSDI). The maximum daily temperature is used to compute both indices. While HWMId is described as the maximum magnitude of the heat waves in a year, WSDI represents the extension of warm spells in a general sense. The results obtained from the regionalised experiments have been evaluated against observational data.
Keywords: decadal prediction, Weather Research and Forecasting Model, heat waves, Iberian Peninsula, dynamical downscaling, Decadal Prediction Large Ensemble
Acknowledgments: J. J. Rosa-Cánovas acknowledges the Spanish Ministry of Science, Innovation and Universities for the predoctoral fellowship (grant code: PRE2018-083921). This research has been carried out in the framework of the projects CGL2017-89836-R, funded by the Spanish Ministry of Economy and Competitiveness with additional FEDER funds, B-RNM-336-UGR18, funded by FEDER / Junta de Andalucía - Consejería de Economía y Conocimiento, and P20_00035, funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades.
How to cite: Rosa-Cánovas, J. J., García-Valdecasas Ojeda, M., Romero-Jiménez, E., Yeste, P., Solano-Farías, F., Gámiz-Fortis, S. R., Castro-Díez, Y., and Esteban-Parra, M. J.: The role of decadal prediction in the detection of heat waves in the Iberian Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10027, https://doi.org/10.5194/egusphere-egu22-10027, 2022.
Fishery sector is of vast importance to the Irish economy. In 2019 it has generated €577 million and employed 16 thousand. The ability to predict changes in the future stock will support adaptation and fish stock management. In decadal climate prediction, initialized predictions have demonstrated improved prediction skill for the North Atlantic. The different stages of fish development are dependent on oceanic variables like temperature and variability and so decadal prediction skill for those variables would allow to make statements on potential changes in fish stock.
Our aim is to improve decadal prediction skill in the Northeast Atlantic. For this we apply ensemble subsampling, a process that selects those ensemble members for creating a subsampled ensemble mean, which perform best under evaluation by physically-based statistical predictors. Climate modes, like Subpolar Gyre (SPG) and the Atlantic Multidecadal Variability (AMV), interact with our region of interest and therefore we will use those to inform us about our subsampling decisions. Applying this methodology on seasonal scales has demonstrated improved prediction skill for other climate modes.
For this contribution we will investigate the application of subsampling on decadal scales for the Northeastern Atlantic on variables like temperature and salinity for different depth levels. The analysis will show how decadal prediction skill will change when wider oceanographic basin information, like SPG and AMV, are considered in the decadal predictions. We will discuss potential implications for a selection of species for the Irish fisheries sector, and with it the possibility for improving the current fish stock management systems in Ireland.
How to cite: O’Beirne, C., Vaughan, L., Koul, V., and Düsterhus, A.: Decadal prediction for Ireland and Irish Fisheries, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8491, https://doi.org/10.5194/egusphere-egu22-8491, 2022.
Recently it has been shown that initialised climate predictions capture the decadal variability of the winter NAO with high skill. However, the signal from models is often hidden among their large internal variability, which results in a low signal-to-noise ratio. In this study, we quantify the skill of the North Atlantic eddy-driven jet’s location and intensity, both in summer and winter. We focus on multi-model decadal predictions made for CMIP6. Overall, we find that models feature a higher skill (as featured by the Anomaly Correlation Coefficient) in predicting the intensity of the jet than its location. For years 2-9, the high winter NAO skill is largely associated with skilful prediction of the jet speed. However, skill in summer is considerably worse than in winter, with models consistently failing to capture the observed southward shift of the Jet between the 1970s and 2010s. Finally, we also show that the skill for the winter NAO is sensitive to the period over which it is computed, and skill drops considerably when evaluating up to the present day, as models fail to capture the observed northern shift and strengthening of the winter eddy-driven jet over the period 2005-2020, as well as the positive trend in the winter NAO.
How to cite: Marcheggiani, A., Robson, J., Monerie, P.-A., Bracegirdle, T., and Smith, D.: Decadal predictability of the North Atlantic eddy-driven jet in winter and summer within CMIP6, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9892, https://doi.org/10.5194/egusphere-egu22-9892, 2022.
We have developed, implemented and preliminary evaluated the performance of the first “eddy-resolving” decadal prediction prototype system based on the MPI-ESM-ER model configuration with the aim to investigate potential improvements due to resolving oceanic eddies in interannual to decadal climate variability and in the prediction skill of the North Atlantic circulation and climate of the regions impacted by it (Europe, Nordic Seas, and Arctic). The MPI-ESM-ER setup is employing an eddy-resolving ocean component with a global resolution of 10 km and an atmospheric component with a resolution of 100 km (T127). The eddy-resolving simulations were compared with similar MPI-ESM-HR experiments conducted within the CMIP6 DCPP-A framework employing an eddy-permitting ocean configuration of 0.4° (~40km). Since both the radiative forcing (CMIP6), the assimilation procedure and ensemble generation are exactly identical, has allowed us to isolate the effect of resolving oceanic eddies (and topographic features) in MPI-ESM-ER prediction system. The variability of the sea surface temperature (SST) in the subpolar North Atlantic over the last decades is well reproduced by the initialized predictions, in contrast to the uninitialized historical simulations. Both prediction systems are able to reproduce the mid-1990s abrupt strong warming event, with a more realistic amplitude of the warming in the MPI-ESM-ER hindcasts. Moreover, there is a clear reduction in the systematic model bias by using an eddy-resolving ocean component in MPI-ESM-ER. All MPI-ESM-HR hindcasts are approximately 1°C too warm, but the MPI-ESM-ER hindcast ensemble is very close to the observations. Reducing the SST bias in the North Atlantic will have implications for other quantities than SST, such as storm tracks or blocking events over Europe. We have also investigated the impact of an “eddy-permitting” and an “eddy resolving” ocean configuration on the predictability of the 2015 record Subpolar North Atlantic “Cold Blob”. Predicting such extreme coupled climate phenomena over the North Atlantic-European region has proved to be very challenging for state-of-art prediction systems. However, we could demonstrate that our prediction system is able to reproduce the observed anomalies, but in years where it is absolutely necessary to forecast the atmosphere conditions too, it will require a large ensemble of hindcasts (of the order of 10 or more): two (out of five) ensemble members in MPI-ESM-HR and six (out of ten) ensemble members in MPI-ESM-ER configuration simulate an eastern subpolar North Atlantic “Cold Blob"" in 2015. One of the MPI-ESM-ER ensemble members even reproduces the full observed strength of the ""Cold Blob"", underlining the potential of high-resolution climate predictions. We could also demonstrate that using an eddy-resolving ocean (0.1°) considerably improves the model systematic bias over the North Atlantic subpolar gyre. Based on these promising results, we plan to investigate the predictability of other recent oceanic extreme climate phenomena.
How to cite: Matei, D., Lohmann, K., Gutjahr, O., and Jungclaus, J.: Towards an "eddy-resolving" climate prediction system, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12731, https://doi.org/10.5194/egusphere-egu22-12731, 2022.
We investigate the forced response of the North Atlantic Oscillation (NAO) in large ensembles of climate models including simulations with historical forcings and initialized decadal hindcasts. The forced NAO in the CMIP6 historical ensemble correlates significantly with observations after 1970. However, the forced NAO shows an apparent non-stationarity with significant correlations to observations only in the period after 1970 and in the period before 1890. We demonstrate that such apparent non-stationarity can be due to chance even when models and observations are independent. We find only weak evidence that initialization improves the skill of the NAO on decadal time-scales. Neither of the historical ensembles including only natural forcings, well-mixed greenhouse-gases, or anthropogenic aerosols show any skillful NAO. Our results question the possibility of useful decadal predictions of the NAO.
How to cite: Christiansen, B., Yang, S., and Matte, D.: On the forced response and decadal predictability of the North Atlantic Oscillation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7508, https://doi.org/10.5194/egusphere-egu22-7508, 2022.
In recent decades three major volcanic eruptions of different intensity have occurred: Mount Agung (1963), El Chichón (1982) and Mount Pinatubo (1991), with reported climate impacts on seasonal-to-decadal timescales and providing a high prediction potential. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of such volcanic eruptions on decadal prediction, which consists in performing initialised sets of predictions just before the three historical volcanic eruptions, but in which the volcanic aerosol forcing is excluded. The impact of the volcanic eruptions is therefore determined by comparing these new forecasts with those included in the corresponding retrospective prediction experiment DCPP-A, which include historical volcanic aerosol forcing. Here we present the results from six CMIP6 decadal prediction systems (CanESM5, CESM1, EC-Earth3, HadGEM3, IPSL-CM6A and CMCC-CM2-SR5). The global mean temperature cooling is comparable among models and consistent with previous studies. The surface temperature response pattern in the first years is similar across all the models and for the individual volcanic eruptions. At later forecast times (years 6-9), differences among the models and eruptions emerge. Preliminary results show that the volcanic eruptions impact the atmospheric and oceanic dynamics, as shown in previous studies, although some differences across models emerge, specifically on the ocean overturning and gyre circulation changes.
How to cite: Bilbao, R., Athanasiadis, P., Hermanson, L., Mignot, J., Sospedra-Alfonso, R., Swingedouw, D., Wu, X., and Ortega, P.: Impact of volcanic eruptions in CMIP6 decadal prediction systems: a multi-model analysis, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13395, https://doi.org/10.5194/egusphere-egu22-13395, 2022.
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.
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.
How to cite: Delgado-Torres, C., Donat, M. G., Gonzalez-Reviriego, N., Caron, L.-P., Athanasiadis, P. J., Bretonnière, P.-A., Dunstone, N. J., Ho, A.-C., Pankatz, K., Paxian, A., Pérez-Zanón, N., Samsó Cabré, M., Solaraju-Murali, B., Soret, A., and Doblas-Reyes, F. J.: Multi-model forecast quality assessment of CMIP6 decadal predictions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13156, https://doi.org/10.5194/egusphere-egu22-13156, 2022.
Decadal prediction of internal (unforced) sea surface temperature (SST) variability relies on proper initialisation of the ocean as well as on the ability of the models to capture the observed internal modes of SST variability. Yet the specific origins of internal decadal SST prediction skill remain unidentified. In this work, we combine physical constraints to allow an a-priori identification of regions that show high actual decadal prediction skill of unforced SST signals.
Specifically, we examine the hypothesis that skillful actual decadal SST prediction requires a combination of: reproduction of large scale persistence of SST in observations by the prediction model; initialization of the ocean state close to observations; and a strong imprint of ocean over atmosphere dynamics on the SST signal. In a MPI-ESM-LR-based decadal prediction system we find that all three criteria are met in the subpolar North Atlantic Ocean, the western Indian Ocean, and the northeast Pacific Ocean. The examined prediction system shows significant skill against HadISST observations in those three regions as well, indicating how the hypothesized physical constraints may identify regions where a decadal prediction system shows actual prediction skill.
Our work shows that internal decadal variations of ocean variables can be predicted beyond the North Atlantic region, highlighting the western Indian Ocean and the northeast Pacific Ocean as potential new hot spots of decadal prediction.
How to cite: Koul, V., Borchert, L., Brune, S., Menary, M., Schrum, C., and Baehr, J.: Physical constraints on actual decadal prediction skill of internal sea surface temperature variability, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13216, https://doi.org/10.5194/egusphere-egu22-13216, 2022.
There is an increasing demand from society and policy makers for reliable, robust and actionable climate information for the upcoming 40 years. However, providing such seamless information poses a challenge to the scientific community. Traditionally, the scientific community developed targeted forecasts for specific time scales, e.g. short-term, seasonal or decadal predictions. These model integrations have thus a limited forecast period and do not provide seamless information on time scales up to 40 years.
This work discusses two alternative approaches to combine information from initialized decadal predictions (providing information up to ten years) with uninitialized climate projections (available until 2100 and beyond).
The first is a novel framework, which is designed to implicitly make use of the (added) values from initialization by constraining uninitialized climate projections using decadal predictions. This approach is applied to near-surface temperatures over the North Atlantic Subpolar gyre region from CMIP5 models. Results suggest that such a constraining approach is able to provide more skillful, seamless climate information beyond decadal time-scales compared to using unconstrained climate projections.
The second approach is based on the simple temporal concatenation of decadal predictions and climate projections. It is shown that this can introduce inconsistencies, which may impact the usability for potential end users. Two different methods to overcome these issues are discussed: the application of a simple calibration method and a weighting scheme based on model performance. Results for the calibration method are in general promising, whereas the impact of the model weighting scheme is smaller. The latter is mainly associated with the small size of the decadal prediction ensemble, which hinders the usual application of the weighting scheme as done in previous studies based on much larger ensembles.
How to cite: Befort, D. J., Brunner, L., Borchert, L. F., O'Reilly, C. H., and Weisheimer, A.: Temporal merging of decadal predictions and climate projections to obtain seamless information: challenges and potential solutions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9477, https://doi.org/10.5194/egusphere-egu22-9477, 2022.
