Challenges in climate prediction: multiple time-scales and the Earth system dimensions

One of the big challenges in Earth system science consists in providing reliable climate predictions on sub-seasonal, seasonal, decadal and longer timescales. The resulting data have the potential to be translated into climate information leading to a better assessment of multi-scale global and regional climate-related risks.
The latest developments and progress in climate forecasting on subseasonal-to-decadal and longer timescales will be discussed and evaluated. This will include presentations and discussions of predictions for a time horizon of up to ten years from dynamical ensemble and statistical/empirical forecast systems, as well as the aspects required for their application: forecast quality assessment, multi-model combination, bias adjustment, downscaling, etc.
Following the new WCPR strategic plan for 2019-2029, prediction enhancements are solicited from contributions embracing climate forecasting from an Earth system science perspective. This includes the study of coupled processes, impacts of coupling and feedbacks, and analysis/verification of the coupled atmosphere-ocean, atmosphere-land, atmosphere-hydrology, atmosphere-chemistry & aerosols, atmosphere-ice, ocean-hydrology, ocean-ice, ocean-chemistry and climate-biosphere (including human component). Contributions are also sought on initialization methods that optimally use observations from different Earth system components, on assessing and mitigating the impacts of model errors on skill, and on ensemble methods.
We also encourage contributions on the use of climate predictions for climate impact assessment, demonstrations of end-user value for climate risk applications and climate-change adaptation and the development of early warning systems.

A special focus will be put on the use of operational climate predictions (C3S, NMME, S2S), results from the CMIP5-CMIP6 decadal prediction experiments, and climate-prediction research and application projects (e.g. EUCP, APPLICATE, PREFACE, MIKLIP, MEDSCOPE, SECLI-FIRM, S2S4E, CONFESS).
An increasingly important aspect for climate forecast's applications is the use of most appropriate downscaling methods, based on dynamical or statistical approaches or their combination, that are needed to generate time series and fields with an appropriate spatial or temporal resolution. This is extensively considered in the session, which therefore brings together scientists from all geoscientific disciplines working on the prediction and application problems.

Co-organized by BG2/CR7/HS13/NH1/NP5
Convener: Andrea Alessandri | Co-conveners: Yoshimitsu Chikamoto, Marlis HoferECSECS, June-Yi Lee, Xiaosong Yang
vPICO presentations
| Fri, 30 Apr, 15:30–17:00 (CEST)

vPICO presentations: Fri, 30 Apr

Chairpersons: Andrea Alessandri, June-Yi Lee, Xiaosong Yang
Decadal Predictions
Tatiana Ilyina, Hongmei Li, Wolfgang Müller, and Aaron Spring

Initialized predictions of near-term future climate have proven successful and predictive power for the global carbon cycle is also emerging. Through extending ESM-based decadal prediction systems, i.e. those contributing to Decadal Climate Prediction Project (DCPP) with the ocean and land carbon cycle components, it becomes possible to establish predictability of the carbon sinks and variations of atmospheric CO2 concentrations. However, such predictions of the global carbon cycle still remain a cutting-edge activity of only a few modeling groups.

On interannual to decadal time-scales, atmospheric CO2 growth rates exhibit pronounced anomalies driven by varying strengths of the land and ocean carbon sinks; these anomalies are linked to climate variability on decadal and interannual time scales. Is it possible to predict if atmospheric CO2 changes slower of faster as expected from changes in emissions? This question is examined in a multi-model framework comprising prediction systems initialized by the observed state of the physical climate. The multi-model framework comprises ESM-based prediction systems that contributed to DCPP within CMIP6, as well as those which run with the CMIP5 forcing.

A predictive skill for the global ocean carbon sink of up to 6 years is found for some models. Longer regional predictability horizons are found across single models. On land, a predictive skill of up to 2 years is primarily maintained in the tropics and extra-tropics enabled by the initialization of the physical climate. Furthermore, anomalies of atmospheric CO2 growth rate inferred from natural variations of the land and ocean carbon sinks are predictable at lead time of 2 years and the skill is limited by the land carbon sink predictability horizon. These predictions of the global carbon cycle and the planet’s breath maintained by variations of atmospheric CO2 are essential to understand where the anthropogenic carbon would go in response to emission reduction efforts addressing global warming mitigation. Such information is useful to verify the effectiveness of fossil fuel emissions reduction measures.

How to cite: Ilyina, T., Li, H., Müller, W., and Spring, A.: Earth system predictions of the carbon sinks and atmospheric CO2 growth: new insights and lessons from DCPP, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2529,, 2021.

Antje Weisheimer, Daniel J. Befort, Lukas Brunner, Leonard F. Borchert, Andrew P. Ballinger, Christopher H. O'Reilly, Gabi Hegerl, and Juliette Mignot

Skillful, reliable and seamless climate information for the next 1-40 years is crucial for policy- and other decision makers to develop suitable planning strategies. This poses a challenge for the scientific community, which is split up into the prediction community (developing initialized predictions up to multi-annual time scales, e.g. 10 years), and the climate projection community (providing long-term projections). As predictions are initialized with the observed climate state at the start of the integration, they are often more skillful for lead times of a few years (depending on variable and region) compared to uninitialized climate projections, which can provide information beyond 10 years. Thus, most useful climate information for the next 1-40 years would likely need to draw upon information from both sources. However, temporal merging from different sources is challenging, e.g., it can lead to discontinuities in the central estimates at the respective transition points, which pose problems for interpretation and communication alike.

The aim of this study is to explore if skillful and seamless climate information can be provided by applying a model weighting scheme to initialized decadal predictions and projections. The model specific weights are based on the respective past model performance compared to observations. Whereas for climate projections each model is assigned a single weight, for initialized decadal predictions these weights are calculated for each forecast year separately. Here, we apply the weighting technique to CMIP6 decadal predictions and climate projections from 8 different models. 

How to cite: Weisheimer, A., Befort, D. J., Brunner, L., Borchert, L. F., Ballinger, A. P., O'Reilly, C. H., Hegerl, G., and Mignot, J.: Can a model weighting scheme be used to obtain skillful, reliable and  seamless climate information for the next 1-40 years?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12737,, 2021.

Hongmei Li, Tatiana Ilyina, Tammas Loughran, and Julia Pongratz

The global carbon budget including CO2 fluxes among different reservoirs and atmospheric carbon growth rate vary substantially in interannual to decadal time-scales. Reconstructing and predicting the variable global carbon cycle is of essential value of tracing the fate of carbon and the corresponding climate and ecosystem changes. For the first time, we extend our prediction system based on the Max Planck Institute Earth system model (MPI-ESM) from concentration-driven to emission-driven taking into account the interactive carbon cycle and hence enabling prognostic atmospheric carbon increment. 

By assimilating atmospheric and oceanic observational data products into MPI-ESM decadal prediction system, we can reproduce the observed variations of the historical global carbon cycle globally. The reconstruction from the fully coupled model enables quantification of global carbon budget within a close Earth system and therefore avoids the budget imbalance term of budgeting the carbon with standalone models. Our reconstructions of carbon budget provide a novel approach for supporting global carbon budget and understanding the dominating processes. Retrospective predictions based on the  emission-driven hindcasts, which are initiated from the reconstructions, show predictive skill in the atmospheric carbon growth rate, air-sea CO2 fluxes, and air-land CO2 fluxes. The air-sea CO2 fluxes have higher predictive skill up to 5 years, and the air-land CO2 fluxes and atmospheric carbon growth rate show predictive skill of 2 years. Our results also suggest predictions based on Earth system models enable reproducing and further predicting the evolution of atmospheric CO2 concentration changes. The earth system predictions will provide valuable inputs for understanding the global carbon cycle and supporting climate relevant policy development. 

How to cite: Li, H., Ilyina, T., Loughran, T., and Pongratz, J.: Reconstructions and predictions of the global carbon cycle with an emission-driven Earth System Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15110,, 2021.

Leilane Passos, Helene R. Langehaug, Marius Årthun, Tor Eldevik, Ingo Bethke, and Madlen Kimmriz

Society's need for operational climate forecast on seasonal to decadal time scales means an increased effort to improve climate prediction models. One way to address this issue is to investigate how initialization techniques affect the predictive skill in these systems.

Considering this, three implementations and two versions of the Norwegian Climate Prediction Model (NorCPM) are analyzed concerning the effects of different initialization methods on the predictive skill in the Arctic-Atlantic region from interannual to decadal time scales. We consider aspects as data assimilation (DA) in the surface vs subsurface, DA update of sea-ice, CMIP5 vs CMIP6 NorCPM versions, ensemble size, and initialization frequency. Besides that, a comparison between the predictive skill in the Norwegian Sea (NS) and the Subpolar North Atlantic Ocean (SPNA) is performed to identify characteristics that can help to improve predictions in these areas.

The additional assimilation of subsurface data increases the predictive skill in the SPNA at all lead times (1-10 years). In contrast, in the NS the skill is increased just at medium lead times (4-7 years). The strongly coupled DA, updating both ocean and sea ice, increases the predictive skill in the SPNA at all lead times, whereas the weakly coupled DA method, only updating ocean, results in higher skill in the NS at shorter (1-3 years) and medium (4-7 years) lead times. With respect to the NorCPM versions, the CMIP5 versions show higher predictive skill in both areas than the CMIP6 ones. In this comparison, besides the differences in the climate forcings, the new NorCPM version contributing to CMIP6 has minor code modifications, addition of interactive aerosol-cloud schemes, and an ocean component with biogeochemistry. Because of that, it is not possible to isolate just the effect of the climate forcings on the skill. Regarding the ensemble size and initialization frequency, NorCPM had a non-linear response; the skill varies with the area, variable, and lead times.

Considering the results, no single version was superior to the others with respect to the skill. In the SPNA, the CMIP5 version, assimilating both surface and subsurface observations, and using strongly coupled DA, shows the highest skill. In the NS, we find the similar except that the highest skill is shown for the weakly coupled DA. Further investigation about technical aspects and the representation of dynamical process are necessary to better understand why the sea ice updating in the strongly coupled method is not beneficial to the NS.

How to cite: Passos, L., R. Langehaug, H., Årthun, M., Eldevik, T., Bethke, I., and Kimmriz, M.: Impact of initialization techniques on the predictive skill of Arctic-Atlantic region in the Norwegian Climate Prediction Model (NorCPM), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15116,, 2021.

Yoshimitsu Chikamoto, Simon Wang, Matt Yost, Larissa Yocom, and Robert Gillies

Skillful multi-year climate forecasts provide crucial information for decision-makers and resource managers to mitigate water scarcity. Yet, such forecasts remain challenging due to unpredictable weather noise and the lack of dynamical model capability. In this research, we demonstrate that the annual water supply of the Colorado River in the United States is predictable up to several years in advance by a drift-free decadal climate prediction system using a fully coupled climate model. Observational analyses and model experiments show that prolonged shortages of water supply in the Colorado River are significantly linked to sea surface temperature precursors, including tropical Pacific cooling, North Pacific warming, and southern tropical Atlantic warming. In the Colorado River basin, the water deficits can reduce crop yield and increase wildfire potential. Thus, a multi-year prediction of severe water shortages in the Colorado River basin could be useful as an early indicator of subsequent agricultural loss and wildfire risk.

How to cite: Chikamoto, Y., Wang, S., Yost, M., Yocom, L., and Gillies, R.: Multi-year predictability of Colorado River water supply using a drift-free decadal climate prediction system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2951,, 2021.

Subseasonal to Seasonal Predictions
John R. Albers, Amy H. Butler, Melissa L. Breeden, Andrew O. Langford, and George N. Kiladis

Mass transport is important to many aspects of Pacific-North American climate, including stratosphere-to-troposphere transport of ozone to the planetary boundary layer, which has negative impacts on human health, and water vapor transport, which contributes to precipitation variability. Here, subseasonal forecasts (forecasts 3-6 weeks into the future) of Pacific jet variability are used to predict stratosphere-to-troposphere transport (STT) and tropical-to-extratropical moisture exports (TME) during boreal spring over the Pacific-North American region. To do this, we consider a very simple conditional probability: if 200 hPa zonal winds have a high (positive or negative) loading on a particular 200 hPa Pacific basin zonal wind pattern, then what will the corresponding shift in the probability of STT or TME be during those time periods? We first answer this question in the context of a retrospective analysis, which allows us to understand the regionality of STT and TME for different jet patterns. Then, using the retrospective results as a guide, we utilize zonal wind hindcasts from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (taken from the S2S Prediction Project) to test whether STT and TME over specific geographic regions may be predictable for subseasonal forecast leads (weeks 3-6). For both analyses, STT and TME are taken from the ETH-Zürich Feature-based climatology database, which allows us to apply a single, self-consistent measure of transport for both the retrospective (1979-2016) and hindcast (1997-2016) analysis periods.

We find that large anomalies in STT to the mid-troposphere over the North Pacific, TME to the west coast of the United States, and TME over Japan are found to have the best potential for subseasonal predictability using upper-level wind forecasts. STT to the planetary boundary layer over the intermountain west of the United States is also potentially predictable for subseasonal leads, but likely only in the context of shifts in the probability of extreme events. While STT and TME forecasts match verifications quite well in terms of spatial structure and anomaly sign, the number of anomalous transport days is underestimated compared to observations. The underestimation of the number of anomalous transport days exhibits a strong seasonal cycle, which becomes progressively worse as spring progresses into summer.

How to cite: Albers, J. R., Butler, A. H., Breeden, M. L., Langford, A. O., and Kiladis, G. N.: Subseasonal prediction of springtime Pacific-North American transport using upper-level wind forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1435,, 2021.

Chloé Prodhomme, Stefano Materia, Constantin Ardilouze, Rachel H. White, Lauriane Batté, Virginie Guemas, Georgios Fragkoulidis, and Javier Garcia Serrano

Under the influence of global warming, heatwaves are becoming a major threat in many parts of the world, affecting human health and mortality, food security, forest fires, biodiversity, energy consumption, as well as the production and transportation networks. Seasonal forecasting is a promising tool to help mitigate these impacts on society. Previous studies have highlighted some predictive capacity of seasonal forecast systems for specific strong heatwaves such as those of 2003 and 2010. To our knowledge, this study is thus the first of its kind to systematically  assess the prediction skill of heatwaves over Europe in a state-of-the-art seasonal forecast system. One major prerequisite to do so is to appropriately define heatwaves. Existing heatwave indices, built to measure heatwave duration and severity, are often designed for specific impacts and thus have limited robustness for an analysis of heatwave variability. In this study, we investigate the seasonal prediction skill of summer heatwave propensity in the ECMWF System 5 operational forecast system (SEAS5) by means of several dedicated metrics as well as its added-value compared to a simple statistical model. We are able to show, for the first time, that seasonal forecasts initialized in early May can provide potentially useful information of summer heatwave propensity.

How to cite: Prodhomme, C., Materia, S., Ardilouze, C., White, R. H., Batté, L., Guemas, V., Fragkoulidis, G., and Garcia Serrano, J.: Seasonal prediction of European Summer Heatwaves, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5725,, 2021.

Noel Keenlyside, Sunil Pariyar, Ingo Bethke, Yiguo Wang, and Francois Counillon

Recent operational systems are able to predict sea surface temperature (SST) on seasonal timescales in the extra-tropical North Atlantic and Nordic Seas to a high-degree and as high as in the tropical Pacific. While prediction on multi-year timescales is well documented, the source of the high skill on seasonal timescales is unclear and somewhat unexpected. Here, using the Norwegian Climate Prediction model, we show that the skill on seasonal timescales is associated primarily with low-frequency variability (timescales longer than five years). Consistently, there is high skill in predicting SST anomalies six seasons in advance, although there is a skill drop across boreal summer that seems associated with reduced vertical mixing. External forcing and initialized ocean variability contribute similarly to skill on seasonal timescales, as assessed through a heat budget analysis. Skill on these timescales can benefit fisheries and aqua culture.

How to cite: Keenlyside, N., Pariyar, S., Bethke, I., Wang, Y., and Counillon, F.: Seasonal prediction in northern Atlantic Ocean and Norwegian Seas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13462,, 2021.

Ignazio Giuntoli, Federico Fabiano, and Susanna Corti

Seasonal predictions in the Mediterranean region have relevant socio-economic implications, especially in the context of a changing climate. To date, sources of predictability have not been sufficiently investigated at the seasonal scale in this region. To fill this gap, we explore sources of predictability using a weather regimes (WRs) framework. The role of WRs in influencing regional weather patterns in the climate state has generated interest in assessing the ability of climate models to reproduce them.

We identify four Mediterranean WRs for the winter (DJF) season and explore their sources of predictability looking at teleconnections with sea surface
temperature (SST). In particular, we assess how SST anomalies affect the WRs frequencies during winter focussing on the two WRs that are associated with the teleconnections in which the signal is more intense: the Meridional and the Anticyclonic regimes . These sources of predictability are sought in five state-of-the-art seasonal forecasting systems included in the Copernicus Climate Change Services (C3S) suite finding a weaker signal but an overall good agreement with reanalysis data. Finally, we assess the ability of the C3S models in reproducing the reanalysis data WRs frequencies finding that their moderate skill improves during ENSO intense years, indicating that this teleconnection is well reproduced by the models and yields improved predictability in the Mediterranean region.

How to cite: Giuntoli, I., Fabiano, F., and Corti, S.: Seasonal predictability of Mediterranean Weather Regimes in the Copernicus C3S Systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15185,, 2021.

Jean-François Guérémy, Clotilde Dubois, Christian Viel, Laurent Dorel, Constantin Ardilouze, Lauriane Batte, Jacques Richon, Yiwen Xu, Fleur Nicolay, Jean-Michel Soubeyroux, and Morgane Le Breton

In the framework of the EU Copernicus Climate Change Service (C3S) program, a new coupled system has been developed at Météo-France (MF) to carry out seasonal forecasts at a 7-month range. This system (called S7) is in operation in real time since October 2019. S7 is based upon the MF coupled climate model CNRM-CM6 used for CMIP6 simulations, in its high resolution configuration: ARPEGE-Climat (Tl359-0.5° l91, including different tuning choices for the physics), NEMO 3.6 (0.25° l75) and the OASIS coupler. The aim of this presentation is twofold.

First, an assessment of S7 performance will be presented in terms of biases, and both deterministic and probabilistic predictability scores. A comparison with the earlier MF system and the current ECMWF system will be shown.

Second, incremental updates from S7 to S8, to be in operation in June 2021, will be presented and assessed versus S7. The upgrade includes a larger atmospheric resolution from l91 to l137, together with a coupled initialization strategy to replace the earlier independent atmospheric and oceanic initialization.

How to cite: Guérémy, J.-F., Dubois, C., Viel, C., Dorel, L., Ardilouze, C., Batte, L., Richon, J., Xu, Y., Nicolay, F., Soubeyroux, J.-M., and Le Breton, M.: Assessment of Météo-France current seasonal forecasting system S7 and outlook on the upcoming S8, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10185,, 2021.

Liwei Jia, Tom Delworth, Sarah Kapnick, Xiaosong Yang, Nathaniel Johnson, William Cooke, Feiyu Lu, Matthew Harrison, Anthony Rosati, Fanrong Zeng, Colleen McHugn, Michell Bushuk, Yongfei Zhang, Andrew Witternberg, Liping Zhang, Hiroyuki Murakami, and Kai-chi Tseng

This study shows that the frequency of North American summer hot days are skillfully predictable months in advance in the newly-developed GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless System for Prediction and EArth System Research) seasonal forecast system. We also demonstrate that climate change, the Pacific Decadal Oscillation (PDO), the Atlantic Multidecadal Oscillation (AMO) and atmosphere-land feedback all contribute to the seasonal predictive skill of the frequency of North American summer hot days. Using a statistical optimization method (Average Predictability Time) we identify two large-scale components of the frequency of North American summer hot days that are predictable with significant correlation skill. One component shows an increase in the frequency of summer hot days everywhere over North America and is highly predictable at least 9 months in advance. This component is related to a secular warming trend. Another predictable component shows largest loadings over the central U.S., and is significantly predictable 6 months ahead. This second component is related to the PDO and the AMO, and is significantly correlated with the central U.S. soil water. These findings have potential implications for predictions of North American summer hot days on seasonal time scales.

How to cite: Jia, L., Delworth, T., Kapnick, S., Yang, X., Johnson, N., Cooke, W., Lu, F., Harrison, M., Rosati, A., Zeng, F., McHugn, C., Bushuk, M., Zhang, Y., Witternberg, A., Zhang, L., Murakami, H., and Tseng, K.: Skillful seasonal prediction of North American summer hot days, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10100,, 2021.

Alice Crespi, Marcello Petitta, Lucas Grigis, Paola Marson, Jean-Michel Soubeyroux, and Christian Viel

Seasonal forecasts provide information on climate conditions several months ahead and therefore they could represent a valuable support for decision making, warning systems as well as for the optimization of industry and energy sectors. However, forecast systems can be affected by systematic biases and have horizontal resolutions which are typically coarser than the spatial scales of the practical applications. For this reason, the reliability of forecasts needs to be carefully assessed before applying and interpreting them for specific applications. In addition, the use of post-processing approaches is recommended in order to improve the representativeness of the large-scale predictions of regional and local climate conditions. The development and evaluation downscaling and bias-correction procedures aiming at improving the skills of the forecasts and the quality of derived climate services is currently an open research field. In this context, we evaluated the skills of ECMWF SEAS5 forecasts of monthly mean temperature, total precipitation and wind speed over Europe and we assessed the skill improvements of calibrated predictions.

For the calibration, we combined a bilinear interpolation and a quantile mapping approach to obtain corrected monthly forecasts on a 0.25°x0.25° grid from the original 1°x1° values. The forecasts were corrected against the reference ERA5 reanalysis over the hindcast period 1993–2016. The processed forecasts were compared over the same domain and period with another calibrated set of ECMWF SEAS5 forecasts obtained by the ADAMONT statistical method.

The skill assessment was performed by means of both deterministic and probabilistic verification metrics evaluated over seasonal forecasted aggregations for the first lead time. Greater skills of the forecast systems in Europe were generally observed in spring and summer, especially for temperature, with a spatial distribution varying with the seasons. The calibration was proved to effectively correct the model biases for all variables, however the metrics not accounting for bias did not show significant improvements in most cases, and in some areas and seasons even small degradations in skills were observed.

The presented study supported the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.

How to cite: Crespi, A., Petitta, M., Grigis, L., Marson, P., Soubeyroux, J.-M., and Viel, C.: Skill assessment of post-processing methods for ECMWF SEAS5 seasonal forecasts over Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11019,, 2021.

Climate Projections
Bo-Joung Park and Seung-Ki Min

Due to the ongoing robust global warming, summer season is expected to get warmer in future over the Northern Hemisphere (NH) land areas. This study examined how the summer season defined by local temperature-based thresholds would change during the 21st century under Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) using Coupled Model Intercomparison Project phase 6 (CMIP6) multiple model simulations. The projection results relative to the current climatology (1995-2014) indicate the significant advance of summer onset and delay of withdrawal over all NH land areas except high latitude locations, with longer than 10 days of summer expansion even in the weakest scenario (SSP1-2.6) in the near-term future (2021-2040). The advance and delay of summer season timing become stronger in the mid-term (2041-2060) and long-term (2081-2020) future periods, ranging from about 10 days to a month depending on SSP scenarios. Largest summer expansion is observed in the middle latitudes, including Europe in high latitude, while the weakest changes are seen over North Asia. Canadian Arctic region is characterized by an asymmetric change with a small advance of summer onset but a relatively large delay in summer ending. CMIP6 models exhibit large inter-model differences, which increase from near-term to long-term future periods. Western North Asia region display larger inter-model difference in summer onset projections while Europe has the largest inter-model spread of summer withdrawal changes. Physical mechanisms associated with these regional and timing-dependent changes in the future summer season lengthening will be further examined.

How to cite: Park, B.-J. and Min, S.-K.: CMIP6 multi-model projection of summer season expansion over the Northern Hemisphere land areas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6870,, 2021.

Shuo Wang, Francois Counillon, Shunya Koseki, Noel Keenlyside, Alok Kumar Gupta, and Maolin Shen

An interactive multi-model ensemble (named as supermodel) based on three state-of-the-art earth system models (i.e., NorESM, MPIESM and CESM) is developed. The models are synchronized every month by data assimilation. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which the covariance matrix is constructed from a historical ensemble. The assimilated data is a weighted combination of the monthly output sea surface temperature (SST) of these individual models, but the full ocean state is constrained by the covariance matrix. The synchronization of the models during the model simulation makes this approach different from the traditional multi-model ensemble approach in which model outputs are combined a-posteriori.

We compare the different approaches to estimate the supermodel weights: equal weights, spatially varying weights based on the minimisation of the bias. The performance of these supermodels is compared to that of the individual models, and multi-model ensemble for the period 1980 to 2006. SST synchronisation is achieved in most oceans and in dynamical regimes such as ENSO. The supermodel with spatially varying weights overperforms the supermodel with equal weights. It reduces the SST bias by over 30% compare to the multi-model ensemble. The temporal variability of the supermodel is slightly on the low side but improved compared to the multi-model ensemble. The simulations are being extended to 2100 to assess the simulation of climate variability and climate change. Performing prediction experiments with the supermodel is the main perspective in the next step.  

How to cite: Wang, S., Counillon, F., Koseki, S., Keenlyside, N., Gupta, A. K., and Shen, M.: Recent development of a supermodel - an interactive multi-model ensemble, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13330,, 2021.

Lukas Brunner, Angeline G. Pendergrass, Flavio Lehner, Anna L. Merrifield, Ruth Lorenz, and Reto Knutti

To extract reliable estimates of future warming and related uncertainties from multi model ensembles such as CMIP6, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the CMIP6 models' historical performance as well as their interdependence, to calculate constrained distributions of global mean temperature change.

We investigate the skill of our approach in a perfect model test framework, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 % compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence.

We then apply the weighting approach, based on two observational estimates, to constrain CMIP6 projections. Our results show a reduction in the projected mean warmingbecause some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7°C, compared with 4.1°C without weighting; the likely (66%) uncertainty range is 3.1 to 4.6°C, which equates to a 13 % decrease in spread.

How to cite: Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced global warming from CMIP6 projections when weighting models by performance and independence, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7717,, 2021.

Ruth Lorenz, Lukas Brunner, Peter Kalverla, Stef Smeets, Jaro Camphuijsen, and Bouwe Andela

Too often model evaluation has no impact on how a multi-model ensemble is analysed. It has been argued that projection and prediction uncertainties can be decreased by giving more weight to those models in multi-model ensembles that are more skillful and realistic for a specific process or application. In addition, some models in multi-model ensembles are not independent and it is not always clear how to include available initial condition ensemble members which are becoming larger in number e.g. in CMIP6.

A weighting approach has been proposed which takes into account both of these aspects (Climate model Weighting by Independence and Performance- ClimWIP) and is able to deal with included initial condition ensemble members. This approach has been shown to decrease uncertainties in multiple use cases such as projections of Arctic September sea ice, North American summer maximum temperatures, European temperature and precipitation, as well as projected global mean temperatures. Even though the basic equation to calculate a model's weight is straight forward, the user needs to make several decisions, such as which metric to use to measure performance or independence, which variables to include etc. and potential pitfalls were identified. For the actual implementation a range of points need to be considered: (1) data from different modelling centers need to be processed and compared in a consistent way, (2) the strength of the performance and independence contributions is determined through two parameters that must also be calibrated, (3) results should be provided in a form that allows backtracing to the original data and code to allow reproducability. To facilitate re-use for new applications, the method was recently implemented into the ESMValTool. We will discuss advantages and disadvantages of the method, show results from some of the use cases, explain how the implementation into ESMValTool was done and how the method can now be more easily used.

How to cite: Lorenz, R., Brunner, L., Kalverla, P., Smeets, S., Camphuijsen, J., and Andela, B.: A multi-model ensemble weighting method (ClimWIP) in ESMValTool, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9387,, 2021.

Sebastian Milinski, Erich Fischer, Piers Forster, John C. Fyfe, June-Yi Lee, Christopher J. Smith, Peter Thorne, Blair Trewin, and Jochem Marotzke

The likelihood of exceeding 1.5 °C of global warming relative to preindustrial depends on the warming observed so far, anthropogenic warming that may occur in the future, and the degree to which internal variability will either temporarily amplify or attenuate future anthropogenic warming.  Here, we introduce a new framework that estimates the likelihood of exceeding 1.5 °C of global warming wherein uncertainties in each one of these factors is explicitly accounted for.

In this new framework, we estimate the historical warming, and its uncertainty, from preindustrial to present using the recently-minted HadCRUT5 dataset. Future anthropogenic warming is estimated using an energy balance model tuned to an assessed range of climate sensitivity and applied to each of the core emissions scenarios (i.e. SSPs) underlying the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). Finally, we estimate the influence of internal variability using a large ensemble of initial condition simulations. On this basis, we find that the largest uncertainty in estimates of the likelihood of exceeding 1.5°C of global warming is due to model-to-model differences in estimates of future anthropogenic warming, followed by historical warming uncertainty, and then uncertainty due to internal variability.

Based on our analysis, we find that the earliest time for crossing 1.5 °C of global warming, here defined as the 5% likelihood, is approximately emissions-scenario independent. We define the 1.5 °C threshold without any overshoot: if a time series warms by more than 1.5 °C during any 20-year period before 2100, it is counted as having crossed 1.5 °C. In each considered scenario except SSP5-8.5, the 20-year average period that crosses the 1.5 °C threshold with a 5% likelihood is 2013 to 2032. On the other hand, the 50% likelihood does depend on the scenario, with the SSP5-8.5 crossing occurring in 2018 to 2037 and SSP1-1.9 crossing in 2022 to 2041. All scenarios except SSP1-1.9 have a likelihood close to 100% to cross 1.5 °C global warming before 2100. Even in SSP1-1.9, the scenario with the strongest emission reductions, there is a 71% likelihood to cross 1.5 °C by the end of this century. This implies that even in SSP1-1.9, the world may stay below 1.5 °C only if both climate sensitivity and historical warming are near the lower end of their respective distributions.

These estimates, with their associated uncertainties, may have major implications for policy decisions.

How to cite: Milinski, S., Fischer, E., Forster, P., Fyfe, J. C., Lee, J.-Y., Smith, C. J., Thorne, P., Trewin, B., and Marotzke, J.: Is 1.5 °C of global warming inevitable and if so, when?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13473,, 2021.

Franco Catalano, Andrea Alessandri, Wilhelm May, and Thomas Reerink

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) aims at diagnosing systematic biases in the land models of CMIP6 Earth System Models and assessing the role of land-atmosphere feedbacks on climate change. Two components of experiments have been designed: the first is devoted to the assessment of the systematic land biases in offline mode (LMIP) while the second component is dedicated to the analysis of the land feedbacks in coupled mode (LFMIP). Here we focus on the LFMIP experiments. In the LFMIP protocol (van den Hurk et al. 2016), which builds upon the GLACE-CMIP configuration, two sets of climate-sensitivity projections have been carried out in amip mode: in the first set (amip-lfmip-pdLC) the land feedbacks to climate change have been disabled by prescribing the soil-moisture states from a climatology derived from “present climate conditions” (1980-2014) while in the second set (amip-lfmip-rmLC) 30-year running mean of land-surface state from the corresponding ScenarioMIP experiment (O’Neill et al., 2016) is prescribed. The two sensitivity simulations span the period 1980-2100 with sea surface temperature and sea-ice conditions prescribed from the first member of historical and ScenarioMIP experiments. Two different scenarios are considered: SSP1-2.6 (f1) and SSP5-8.5 (f2).

In this analysis, we focus on the differences between amip-lfmip-rmLC and amip-lfmip-pdLC at the end of the 21st Century (2071–2100) in order to isolate the impact of the soil moisture changes on surface climate change. The (2071-2100) minus (1985-2014) temperature change is positive everywhere over land and the climate change signal of precipitation displays a clear intensification of the hydrological cycle in the Northern Hemisphere. Warming and hydrological cycle intensification are larger in SSP5-8.5 scenario. Results show large differences in the feedbacks between wet, transition and semi-arid climates. In particular, over the regions with negative soil moisture change, the 2m-temperature increases significantly while the cooling signal is not significant over all the regions getting wetter. In agreement with Catalano et al. (2016), the larger effects on precipitation due to soil moisture forcing occur mostly over transition zones between dry and wet climates, where evaporation is highly sensitive to soil moisture. The sensitivity of both 2m-temperature and precipitation to soil moisture change is much stronger in the SSP5-8.5 scenario.

How to cite: Catalano, F., Alessandri, A., May, W., and Reerink, T.: Land-surface feedbacks on temperature and precipitation in CMIP6-LS3MIP projections, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12175,, 2021.

Andrea Lira Loarca and Giovanni Besio

Global and regional climate models are the primary tools to investigate the climate system response to different scenarios and therefore allow to make future projections of different atmospheric variables which are used as input for wave generation models to assess future wave climate. Adequate projections of future wave climate are needed in order to analyze climate change impacts and hazards in coastal areas such as flooding and erosion with waves being the predominant factor with varied temporal variability. 

Bias adjustment methods are commonly used for climate impact variables dealing with systematic errors (biases) found in global and regional climate models.  While bias correction techniques are extended in the climate and hydrological impact modeling scientific communities, there is still a lack of consensus regarding their use in sea climate variables (Parker & Hill, 2017; Lemos et al, 2020; Lira-Loarca et at, 2021)

In these work we assess the performance of different bias-adjustment methods such as the Empirical Gumbel Quantile Mapping (EGQM) method as a standard method which takes into the account the extreme values of the distribution takes, the Distribution Mapping method using Stationary Mixture Distributions (DM-stMix) allowing for a better representation of each variable in the mean regime and tails and the Distribution Mapping method using Non-Stationary Mixture Distributions (DM-nonstMix) as an improved methods which allows to take into account the temporal variability of wave climate according to different baseline periods such as monthly, seasonal, yearly and decadal. The performance of the different bias adjustment methods will be analyzed with particular interest on the futural temporal behavior of wave climate. The advantages and drawbacks of each bias adjustment method as well as their complexity will be discussed.



  • Lemos, G., Menendez, M., Semedo, A., Camus, P., Hemer, M., Dobrynin, M., Miranda, P.M.A. (2020). On the need of bias correction methods for wave climate projections, Global and Planetary Change, 186, 103109.
  • Lira-Loarca, A., Cobos, M., Besio, G., Baquerizo, A. (2021) Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess.
  • Parker, K. & Hill, D.F. (2017) Evaluation of bias correction methods for wave modeling output, Ocean Modelling 110, 52-65

How to cite: Lira Loarca, A. and Besio, G.: Performance evaluation of bias adjustment methods for wave climate projections in the Mediterranean Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13557,, 2021.

Processes Understanding and Implementation
Fransje van Oorschot, Ruud van der Ent, Andrea Alessandri, and Markus Hrachowitz

The root zone storage capacity (Sr ) is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. Many studies have shown that Sr is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Root zone parameterization in most land surface models does not account for this climate control on root development, being based on look-up tables that prescribe worldwide the same root zone parameters for each vegetation class. These look-up tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. The objective of this research was to quantify and evaluate the effects of a climate-controlled representation of Sr on the  water fluxes modeled by the HTESSEL land surface model. Climate controlled Sr was here estimated with the "memory method" (hereinafter MM) in which Sr is derived from the vegetation's memory of past root zone water storage deficits. Sr,MM was estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of Sr,MM were then implemented in HTESSEL (hereinafter MD) by accordingly modifying the soil depths to obtain a model Sr,MD that matches Sr,MM in the 15 catchments. In the control version of HTESSEL (hereinafter CTR), Sr,CTR was larger than Sr,MM in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of Sr,MM (117—722 mm) was considerably larger than of Sr,CTR (491—725 mm). The HTESSEL MD version resulted in a significant and consistent improvement version of the modeled monthly seasonal climatology (1975--2010) and inter-annual anomalies of river discharge compared with observations. However, the effects on biases in long-term annual mean fluxes were small and mixed. The modeled monthly seasonal climatology of the catchment discharge improved in MD compared to CTR: the correlation with observations increased significantly from 0.84 to 0.90 in tropical catchments, from 0.74 to 0.86 in temperate catchments and from 0.86 to 0.96 in Mediterranean catchments. Correspondingly, the correlations of the inter-annual discharge anomalies improved significantly in MD from 0.74 to 0.78 in tropical catchments, from 0.80 to 0.85 in temperate catchments and from 0.71 to 0.79 in Mediterranean catchments. Based on these results, we believe that a global application of climate controlled root zone parameters has the potential to improve the timing of modeled water fluxes by land surface models, but a significant reduction of biases is not expected. 

How to cite: van Oorschot, F., van der Ent, R., Alessandri, A., and Hrachowitz, M.: Climate controlled root zone parameters show potential to improve water flux simulations by land surface models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2883,, 2021.

Roberto Bilbao, Magdalena Balmaseda, Lauriane Batte, Markus Donat, Pablo Ortega, Etienne Tourigny, and Tim Stockdale

Explosive volcanic eruptions have climate impacts on seasonal-to-decadal time-scales. Studies have shown that these climate impacts have high predictive potential, and could therefore be exploited to improve operational climate predictions whenever a new explosive volcanic eruption happens. In preparation for such an event, which has occurred three times in the last 60 years, it is necessary to develop the capability to estimate and ingest the associated stratospheric volcanic forcing into the operational seasonal-to-decadal forecasts systems. This is one of the objectives of the H2020 project CONFESS (CONsistent representation of temporal variations of boundary Forcings in reanalysES and Seasonal forecasts), for which the main tasks envisaged are presented herein. The first task involves several technical developments in the IFS (the atmospheric model of the European Center for Medium-range Weather Forecasting) to improve the model representation of volcanic aerosols. Since for a new major volcanic eruption the evolution and distribution of the volcanic aerosols is initially unknown, the second task is to evaluate a method to estimate them based on several assumptions. For this purpose the recently enhanced emulator of volcanic aerosols EVA_H (Aubrey et al., 2019) will be used to produce the stratospheric volcanic aerosol forcing. In a final task, the outputs of the EVA_H module will be validated by producing the forcings of the past volcanic eruptions of Agung, El Chichon and Pinatubo, and the realism of their climate response will be evaluated in seasonal and multi-annual re-forecasts.

How to cite: Bilbao, R., Balmaseda, M., Batte, L., Donat, M., Ortega, P., Tourigny, E., and Stockdale, T.: Implementing the capability to respond to large volcanic eruptions in the C3S prediction systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16465,, 2021.

Andrea Alessandri, Franco Catalano, Matteo De Felice, Bart van den Hurk, and Gianpaolo Balsamo

Changes in snow and vegetation cover associated with global warming can modify surface albedo (the reflected amount of radiative energy from the sun), therefore modulating the rise of surface temperature that is primarily caused by anthropogenic greenhouse-gases emission. This introduces a series of potential feedbacks to regional warming with positive (negative) feedbacks enhancing (reducing) temperature increase by augmenting (decreasing) the absorption of short-wave radiation. So far our knowledge on the importance and magnitude of these feedbacks has been hampered by the limited availability of relatively long records of continuous satellite observations.

Here we exploit a 31-year (1982-2012) high-frequency observational record of land data to quantify the strength of the surface-albedo feedback on land warming modulated by snow and vegetation during the recent historical period. To distinguish snow and vegetation contributions to this feedback, we examine temporal composites of satellite data in three different Northern Hemisphere domains. The analysis reveals and quantifies markedly different signatures of the surface-albedo feedback. A large positive surface-albedo feedback of +0.87 [CI 95%: 0.68, 1.05] W/(m2∗K) absorbed solar radiation per degree of temperature increase is estimated in the domain where snow dominates. On the other hand the surface-albedo feedback becomes predominantly negative where vegetation dominates: it is largely negative (-0.91 [-0.81, -1.03] W/(m2∗K)) in the domain with vegetation dominating, while it is moderately negative (-0.57 [-0.40, -0.72] W/(m2∗K)) where both vegetation and snow are significantly present.  Snow cover reduction consistently provides a positive feedback on warming. In contrast, vegetation expansion can produce either positive or negative feedbacks in different regions and seasons, depending on whether the underlying surface being replaced has higher (e.g. snow) or lower (e.g. dark soils) albedo than vegetation.

The observational data and analysis from this work is supplying fundamental knowledge to model and predict how the surface-albedo feedback will evolve and affect the rate of regional temperature rise in the future. So far the simulation and prediction of albedo feedbacks shows a large spread and divergence among the available state-of-the-art Earth System Models (ESMs), due to uncertainties in the representation of vegetation-snow processes and the dynamics of vegetation and to uncertainties in land-cover parameters. By exploiting the unprecedented observational benchmarks to evaluate the ESMs currently engaged in CMIP6, this work will allow an improved and better constrained representation of the processes underlying surface albedo feedbacks in the next generation of ESMs. 

This work is in now in Press and Open Access on Environmental Research Letters:

How to cite: Alessandri, A., Catalano, F., De Felice, M., van den Hurk, B., and Balsamo, G.: Varying Snow and Vegetation Signatures of Surface Albedo Feedback on the Northern Hemisphere Land Warming, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13456,, 2021.