CL3.1.9

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, https://doi.org/10.5194/egusphere-egu21-2529, 2021.

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, https://doi.org/10.5194/egusphere-egu21-2951, 2021.

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, https://doi.org/10.5194/egusphere-egu21-5725, 2021.

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, https://doi.org/10.5194/egusphere-egu21-13462, 2021.

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, https://doi.org/10.5194/egusphere-egu21-15185, 2021.

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, https://doi.org/10.5194/egusphere-egu21-10185, 2021.

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, https://doi.org/10.5194/egusphere-egu21-10100, 2021.

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, https://doi.org/10.5194/egusphere-egu21-6870, 2021.

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, https://doi.org/10.5194/egusphere-egu21-13330, 2021.

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, https://doi.org/10.5194/egusphere-egu21-7717, 2021.

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, https://doi.org/10.5194/egusphere-egu21-9387, 2021.

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, https://doi.org/10.5194/egusphere-egu21-12175, 2021.

The root zone storage capacity (S_r ) 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 S_r 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 S_r on the  water fluxes modeled by the HTESSEL land surface model. Climate controlled S_r was here estimated with the "memory method" (hereinafter MM) in which S_r is derived from the vegetation's memory of past root zone water storage deficits. S_r,MM was estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of S_r,MM were then implemented in HTESSEL (hereinafter MD) by accordingly modifying the soil depths to obtain a model S_r,MD that matches S_r,MM in the 15 catchments. In the control version of HTESSEL (hereinafter CTR), S_r,CTR was larger than S_r,MM in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of S_r,MM (117—722 mm) was considerably larger than of S_r,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, https://doi.org/10.5194/egusphere-egu21-2883, 2021.

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, https://doi.org/10.5194/egusphere-egu21-13456, 2021.