Predictions of climate from seasonal to decadal time scales and their applications will be discussed in this session. With a time horizon from a few months up to thirty years, such predictions are of major importance to society, and improving them presents an interesting scientific challenge. This session aims to embrace advances in our understanding of the origins of seasonal to decadal predictability, as well as in improving the respective forecast skill and making the most of this information by building and testing new applications and climate services.
The session will cover dynamical as well as statistical predictions (including machine learning methods), and their combination. It will investigate predictions of various climate phenomena, including extremes, from global to regional scales, and from seasonal to multidecadal time scales ("seamless predictions"). Physical processes relevant to long-term predictability sources (e.g. ocean, cryosphere, or land) as well as predicting large-scale atmospheric circulation anomalies associated to teleconnections will be discussed, as will observational and emergent constraints on climate variability and predictability on the seasonal-to-(multi)decadal time scale. Also, the time-dependence of the predictive skill, or windows of opportunity (hindcast period), will be investigated. Analysis of predictions in a multi-model framework, and ensemble forecast initialization and generation, including innovative ensemble approaches to minimize initialization shocks, will be another focus of the session. The session will pay particular attention to innovative methods of quality assessment and verification of climate predictions, including extreme-weather frequencies, post-processing of climate hindcasts and forecasts, and quantification and interpretation of model uncertainty. We particularly invite contributions presenting the use of seasonal-to-decadal predictions for risk assessment, adaptation and further applications.
vPICO presentations: Mon, 26 Apr
Predictability of sea surface temperatures (SSTs) in the North Atlantic on timescales on several years and beyond is commonly attributed to buoyancy-forced changes of the Atlantic Meridional Overturning Circulation and associated poleward heat transport.
We examine the role of the wind stress anomalies in decadal hindcasts for the prediction of annual SST anomalies in the extratropical North Atlantic. A global climate model (KCM) is forced by ERA-interim wind stress anomalies over the period 1979-2017. The resulting climate states serve as initial conditions for decadal hindcasts.
We find significant skill in predicting annual SST anomalies over the central extratropical North Atlantic with anomaly correlation coefficients exceeding 0.6 at lead times of 4 to 7 years. The skill of annual SSTs is basically insensitive to the calendar month of initialization. We suggest that this skill is linked to a gyre-driven upper-ocean heat content anomaly that leads anomalous SSTs by several years.
Furthermore, another set of model experiments, employing a freshwater flux correction, will be assessed. Freshwater flux correction has been shown to improve the model’s mean state of North Atlantic surface properties and of the circulation. We will address the potentially improved predictability and underlying mechanisms.
How to cite: Reintges, A., Latif, M., Bordbar, M. H., and Park, W.: Wind Stress-Induced Multiyear Predictability of Annual Sea Surface Temperature Anomalies in the Extratropical North Atlantic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-737, https://doi.org/10.5194/egusphere-egu21-737, 2021.
While decadal North Atlantic sea surface temperature (SST) variations are generally predictable, prediction skill of surface temperature over Europe is much more limited. We invoke here observed links of decadal European summer temperature variations to North Atlantic SST changes in the preceding months to produce skillful decadal predictions of European summer temperature variations.
We analyze the ERA5 reanalysis data set to re-assess the observed influence of North Atlantic SST on European summer temperature for the period 1960-2020. To facilitate possible merging activities of initialized decadal prediction simulations and climate projections in the future, we examine predictions for the target regions Northern Europe (NEU), Central Europe (CEU) and Mediterranean (MED) as are defined as the SREX regions for IPCC Assessment Report 5. Summer (June-July-August: JJA) temperature in NEU shows significant co-variability in a decadal spectral band with MAM SST in the Western North Atlantic (WNA), while JJA CEU temperature shows the same with JJA SST in that region. JJA temperature in the MED region shows significant decadal co-variability with the annual mean AMV index. SVD analysis illustrates that an atmospheric Rossby wave train connects North Atlantic SST to European summer temperature changes.
Dynamical retrospective forecasts from a suite of decadal prediction systems from the Coupled Model Intercomparison Project Phase 6 Decadal Climate Prediction Project are tested for their agreement with observations for the period 1960-2020. Dynamical predictions of JJA temperature in NEU, CEU and MED are mostly not skillful at lead years 1-10 in the CMIP6 simulations. Most models do, however, show skill in the SST regions that are connected to these summer temperature variations, identified above. We use these SST predictions to drive a simple statistical model that rescales the variance of the SST predictions according to observed SAT variance in the target region. This dynamical-statistical prediction is shown to be skillful at lead years 1-10 for summer temperature in the SREX regions. This skill, however, relies on the skill of the models in predicting the respective SST index. Our work therefore indicates a promising avenue to produce skillful decadal climate predictions over land based on skillful predictions of the ocean.
How to cite: Mignot, J., Borchert, L., Koul, V., Mayer, B., Menary, M., Sgubin, G., and Swingedouw, D.: Skillful Dynamical-Statistical Predictions of European Summer Temperature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10788, https://doi.org/10.5194/egusphere-egu21-10788, 2021.
Marine Heatwaves (MHWs) are Sea Surface Temperature (SST) extremes that can have devastating impacts on marine ecosystems but can also impact circulation patterns in the ocean and the atmosphere. The variability of MHWs has been studied in historical observations and longterm climate projections, but predictability has only been analyzed on seasonal timescales. Here, we we present the first attempt to study the decadal predictability of MHW days per year in an ensemble of decadal hindcasts based on the Max Planck Institute Earth System Model (MPI-ESM-LR).
Our results show that there are strong regional differences in prediction skill. While many regions show little to no skill, we find in the Subpolar North Atlantic correlation coefficients up to 0.7 for MHW days up to lead year 8. We demonstrate that these correlations mainly arise from correctly predicting the absence of MHWs in individual years. MHW days per year might be successfully predicted by only using yearly mean SST as a proxy, which also demonstrates that in the Subpolar North Atlantic, any increase in SST is accompanied by more MHWs and vice versa.
How to cite: Hövel, L., Brune, S., and Baehr, J.: Decadal Prediction of Marine Heatwaves in MPI-ESM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3844, https://doi.org/10.5194/egusphere-egu21-3844, 2021.
Decadal predictions have become essential for near-term decision making and adaptation strategies. In parallel, interest in weather and climate extremes has increased strongly in the past. Thus, a combination of decadal predictions and extreme value theory is reasonable and necessary. Since decadal predictions suffer from typical discrepancies, such as start- and lead-year dependent conditional and unconditional biases, many ways for their recalibration have been proposed (Eade et al., 2014; Fučkar et al.,2014; Fyfe et al., 2011; Kharin et al., 2012; Kruschke et al., 2016; Raftery et al., 2005; Sansom et al., 2016; Sloughter et al., 2007). However, in previous studies, extremes have not been considered. Therefore, the aim of this study is to investigate how extremes from decadal predictions can be adequately recalibrated and how this affects forecasting skill. Pasternack et al. (2018) introduced a parametric Decadal Climate Forecast Recalibration Strategy (DeFoReSt 1.0), based on estimating polynomial adjustment terms (Gangstø et al., 2013). DeFoReSt assumes normality for the probability distribution (PDF) to be recalibrated and optimizes the cross-validated continuous ranked probability score (CRPS) with this assumption build in Gneiting et al. (2005). For a proof of concept, Pasternack et al. (2018) introduced a toy model for generating pseudo decadal forecast-observation pairs. For toy model data and surface temperatures from MiKlip hindcasts, improvement of forecast quality over a simple calibration from Kruschke et al. (2016) has been found. We extend these methods to extreme values with two modifications: (1) Follow DeFoReSt, but assume general extreme value (GEV) distributed forecasts. Again the CRPS is optimized but with the GEV build into the score (Friederichs and Thorarinsdottir, 2012). Both DeFoReSt strategies (DeFoReSt-normaland DeFoReSt-GEV) and the calibration from Kruschke et al. (2016) are compared to a forecast based on climatology. (2) The toy model is modified to generate pseudo decadal forecast-observation pairs with GEV distributed observations. For validation, a bootstrapping scheme is applied to temperature maxima hindcasts from MiKlip verified with HadEX2 observations. After recalibration, both DeFoReSt strategies perform similar for the toy model and MiKlip hindcasts, none significantly outperforms the other. However, they consistently show considerable improvements over the climatological forecast for the lower and upper quartiles in the toy model data. For the recalibrated MiKlip hindcasts, the findings are in accordance, but not as considerable, presumably due to their very small ensemble size (Sienz et al., 2016). This suggests that extremes may be directly recalibrated with the assumption of a Normal distribution, as long as this represents the characteristics of the decadal forecast ensemble. Thus, the forecasting skill of recalibrations appears to be unaffected by the underlying distribution of the observations.
How to cite: Magro, F.-A., Pasternack, A., and Rust, H. W.: Recalibrating Extremes for Decadal Predictions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10033, https://doi.org/10.5194/egusphere-egu21-10033, 2021.
We study the decadal predictability in the North Atlantic region using ensembles of historical and decadal prediction experiments with EC-Earth3 and other CMIP models. In particular, the focus is on the NAO and the sub-polar gyre region. In general the impact of initialization is weak for lead-times larger than one to two years and we investigate different ways to isolate and estimate the statistical significance of this impact. For the sub-polar gyre region the prediction skill is found to be mainly due to an abrupt change in the late 90ies and models disagree on whether this skill is due to forcing or initial conditions. Also the predictability of the NAO is weak and varies with lead-time and length of the predicted period. We only see weak evidence of the 'signal-to-noise paradox'. The importance of the ensemble size is also studied.
How to cite: Christiansen, B., Yang, S., and Matte, D.: Decadal predictability in the North Atlantic as seen by EC-Earth3., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11950, https://doi.org/10.5194/egusphere-egu21-11950, 2021.
The Irish sea food sector and the associated planning and managing of the fisheries sector is of great importance for the Irish economy. In decadal climate prediction the North Atlantic has already shown significant prediction skill for initialized predictions. However, making them applicable for a wider community is a challenge. For this a better understanding of the North Atlantic mechanisms, like the sub-polar gyre (SPG) is essential, as those systems have higher predictability as the local environmental factors for the fish themselves.
Primary focus is the investigation of the environmental impact factors for the target species and the capability of the decadal prediction system to predict them. Besides the usual variables, like surface temperature (SST) or surface salinity, it is important to take a look at their predictability in the depth. Further analysis would then allow to investigate how this predictability can be increased by mechanisms acting on a larger scale. A final step will be to tailor the predictions for the Irish fisheries sector.
In this contribution, we will show how the decadal prediction system based on the Max Planck Institute Earth System Model (MPI-ESM) is able to predict oceanographic variables like temperature and salinity in the North East Atlantic. This will allow us to get an insight into the potential predictability of important 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.: Potential application of decadal prediction for Irish fisheries, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5101, https://doi.org/10.5194/egusphere-egu21-5101, 2021.
Current state-of-the-art decadal ensemble prediction systems are run with an ensemble size of 10 to 40 members, their retrospective forecasts of the past are used to assess the system's prediction skill. Here, we present an attempt for a large ensemble decadal prediction system for the time period 1960-today, with an ensemble size of 80 members, based on the low resolution version of the Max Planck Institute Earth system model (MPI-ESM-LR). The ensemble is forced with CMIP6 conditions and initialized every year in November through a weakly coupled assimilation using atmospheric reanalyses via nudging and observed oceanic temperature and salinity profiles via a 16-member ensemble Kalman filter. To generate ensemble members beyond 16, we use additional physical perturbations at stratospheric height. The analysis of our large ensemble prediction system presented here aims for answering two questions: (1) How does the ensemble mean deterministic prediction skill for global and North Atlantic key climate indices change with ensemble size? (2) How well may the 80-member ensemble serve as a basis for a robust statistical analysis of probabilities of extremes in the North Atlantic sector? Preliminary results for global and regional air surface temperature show that in terms of ensemble mean ACC and full ensemble CPRSS with reference data, the 80-member ensemble leads to similar prediction skill as the 16-member ensemble. This indicates that the additional ensemble members may lead to a better sampling of the distribution of model trajectories, paving the way for a more robust statistical probabilistic analysis.
How to cite: Brune, S., Koul, V., Nielsen, D. M., Hövel, L., Pohlmann, H., Düsterhus, A., and Baehr, J.: A large ensemble decadal prediction system with MPI-ESM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9263, https://doi.org/10.5194/egusphere-egu21-9263, 2021.
The land-atmosphere CO2 exchange exhibits a very high interannual variability which dominates variability in atmospheric CO2 concentration. Despite efforts to decrease the discrepancy between simulated and observed terrestrial carbon fluxes, the uncertainty in trends and patterns of the land carbon fluxes remains high. This difficulty raises the question to what extent the terrestrial carbon cycle is even predictable, and which processes explain the predictability. In this study, the perfect model approach is used to assess the potential predictability of net primary production (NPP) and heterotrophic respiration (Rh) by using initialized ensemble experiments simulated with the Max Planck Institute Earth System Model. In order to determine which processes are causing the derived predictability patterns, carbon flux predictability was decomposed into individual drivers. Regression analysis was used to determine the contribution of the predictability of different environmental drivers to the predictability of NPP and Rh (Soil moisture, temperature and radiation for NPP and soil organic carbon, temperature and precipitation for Rh). The main drivers of NPP predictability are soil moisture and temperature, while the predictability signal from radiation is lost after the first month of simulation. Rh predictability is predominantly driven by soil organic carbon, temperature and locally by precipitation. This decomposition of predictability shows that the relatively high Rh predictability is due to the generally high predictability of soil organic carbon. The assessed seasonality in predictability patterns can be explained by the change in limiting factors of NPP and Rh over the wet and dry months. This leads to the adjustment of carbon flux predictability to the predictability of the currently limiting environmental factor. Differences in the predictability between initializations can be attributed to the interannual variability in soil moisture and temperature predictability. This variability is caused by the state dependency of nonlinear ecosystem processes. These results reveal the crucial regions and ecosystem processes to be considered when initializing a carbon prediction system.
How to cite: Dunkl, I., Spring, A., and Brovkin, V.: Process-based analysis of land carbon flux predictability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2093, https://doi.org/10.5194/egusphere-egu21-2093, 2021.
Glaciers fulfil several important roles in the earth system, including being clear indicators of climate change and providing essential freshwater storage and downstream runoff to 22% of the global population. In addition, they are the main contributors to sea level rise and are expected to remain so throughout the 21st Century. In order to monitor glacier development, observing and predicting glacier mass balance on different spatial and temporal scales is essential. The current study aims to improve the understanding of glacier mass balance prediction on the decadal scale (5-15 years), a rarely studied time scale in the context of glaciers, but if reliable, highly applicable for glacier related water resource management and sea level rise predictions. This is achieved through the use of CMIP5 decadal climate prediction multi-model ensembles (reforecasts) to force the mass balance component of the Open Global Glacier Model (OGGM). This method is applied to 254 reference glaciers, distributed throughout 17 of the 19 Randolph Glacier Inventory (RGI) regions. The reforecasts are initialized in 1960 and 1980 and bias corrected to the glacier scale. The following statistical analysis then gives a good indication of the skill of climate reforecasts in mass balance modelling on this glacier atypical time scale.
How to cite: van der Laan, L., Förster, K., Maussion, F., and Scaife, A.: Using a CMIP5 multi-model ensemble to model glacier mass balance on decadal scales, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10047, https://doi.org/10.5194/egusphere-egu21-10047, 2021.
As the leading climate mode to explain wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Northern Hemispheric cryosphere as possible predictor for the wintertime NAO (Cohen et al. 2014). Although several studies could find seasonal prediction skill in reanalysis data (Orsolini et al. 2016, Duville et al. 2017,Han & Sun 2018), experiments with ocean-atmosphere general circulation models (AOGCMs) still show conflicting results (Furtado et al. 2015, Handorf et al. 2015, Francis 2017, Gastineau et al. 2017).
Here we use two kinds ECMWF seasonal prediction ensembles starting with November initial conditions taken from the long-term reanalysis ERA-20C and forecasting the following three winter months. Besides the 110-year ensemble of 50 members representing internal variability of the atmosphere, we investigate a second ensemble of 20 members where initial conditions are split between low and high snow cover years for the Northern Hemisphere. We compare two recently used Eurasian snow cover indices for their skill in predicting winter climate for the European continent. Analyzing the two forecast experiments, we found that prediction runs starting with high snow index values in November result in significantly more negative NAO states in the following winter (DJF), which in turn modulates near surface temperatures. We track the atmospheric anomalies triggered by the high snow index through the tropo- and stratosphere as well as for the individual winter months to provide a physical explanation for the formation of this particular climate state.
How to cite: Wegmann, M., Orsolini, Y., Weisheimer, A., van den Hurk, B., and Lohmann, G.: Forecast skill of autumn snow for European winter climate during the 20th century: A multi member seasonal prediction experiment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2586, https://doi.org/10.5194/egusphere-egu21-2586, 2021.
We investigate the impact of large climatological biases in the tropical Atlantic on reanalysis and seasonal prediction performance using the Norwegian Climate Prediction Model (NorCPM) in a standard and an anomaly coupled configuration. Anomaly coupling corrects the climatological surface wind and sea surface temperature (SST) fields exchanged between oceanic and atmospheric models, and thereby significantly reduces the climatological model biases of precipitation and SST. NorCPM combines the Norwegian Earth system model (NorESM) with the Ensemble Kalman Filter and assimilates SST and hydrographic profiles. We perform a reanalysis for the period 1980-2010 and a set of seasonal predictions for the period 1985-2010 with both model configurations. Anomaly coupling improves the accuracy and the reliability of the reanalysis in the tropical Atlantic, because the corrected model enables a dynamical reconstruction that satisfies better the observations and their uncertainty. Anomaly coupling also enhances seasonal prediction skill in the equatorial Atlantic to the level of the best models of the North American multi-model ensemble, while the standard model is among the worst. However, anomaly coupling slightly damps the amplitude of Atlantic Niño and Niña events. The skill enhancements achieved by anomaly coupling are largest for forecast started from August and February. There is strong spring predictability barrier, with little skill in predicting conditions in June. The anomaly coupled system show some skill in predicting the secondary Atlantic Niño-II SST variability that peaks in November-December from August 1st.
How to cite: Counillon, F., Keenlyside, N., Toniazzo, T., Koseki, S., Demissie, T., Bethke, I., and Wang, Y.: Relating model bias and prediction skill in the equatorial Atlantic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10380, https://doi.org/10.5194/egusphere-egu21-10380, 2021.
We analyse the connections between the wintertime North Atlantic Oscillation (NAO), the eddy-driven jet stream with the mid-latitude cyclonic activity over the North Atlantic and Europe. We investigate, through the comparison against ECMWF ERA5 and hindcast simulations from the Max Planck Institute Earth System Model (MPI-ESM), the potential for enhancement of the seasonal prediction skill of the Eddy Kinetic Energy (EKE) by accounting for the connections between large-scale climate and the regional cyclonic activity. Our analysis focuses on the wintertime months (December-March) in the 1979-2019 period, with seasonal predictions initialized every November 1st. We calculate EKE from wind speeds at 250 hPa, which we use as a proxy for cyclonic activity. The zonal and meridional wind speeds are bandpass filtered with a cut-off at 3-10 days to fit with the average lifespan of mid-latitude cyclones.
Preliminary results suggest that in ERA5, major positive anomalies in EKE, both in quantity and duration, are correlated with a northern position of the jet stream and a positive phase of the NAO. Apparently, a deepened Icelandic low-pressure system offers favourable conditions for mid-latitude cyclones in terms of growth and average lifespan. In contrast, negative anomalies in EKE over the North Atlantic and Central Europe are associated with a more equatorward jet stream, these are also linked to a negative phase of the NAO. Thus, in ERA5, the eddy-driven jet stream and the NAO play a significant role in the spatial and temporal distribution of wintertime mid-latitude cyclonic activity over the North Atlantic and Europe. We extend this connection to the MPI-ESM hindcast simulations and present an analysis of their predictive skill of EKE for wintertime months.
How to cite: Aranyossy, A., Brune, S., Hellmich, L., and Baehr, J.: Seasonal Predictability of Wintertime mid-latitude Cyclonic Activity over the North Atlantic and Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11134, https://doi.org/10.5194/egusphere-egu21-11134, 2021.
Teleconnections are sources of predictability for regional weather and climate, which can be represented by causal relationships between climate features in physically separated regions. In this study, teleconnections of low rainfall anomalies in Indonesian Borneo are analysed and quantified using causal inference theory and causal networks. Causal hypotheses are first developed based on climate model experiments in literature and then justified by means of partial regression analysis between NCEP reanalysis sea surface temperatures and climate indices (drivers) and rainfall data in Indonesian Borneo from various sources (target variable). We find that, as previous studies have highlighted, El Niño Southern Oscillation (ENSO) has a profound effect on rainfall in Indonesia Borneo, with positive Niño 3.4 index serving as a direct driver of low rainfall, also partially through reduced sea surface temperatures (SSTs) over Indonesian waters. On the other hand, while Indian Ocean Dipole (IOD) influences Indonesian Borneo rainfall through SSTs over the same area as a thermodynamic effect, its remaining effect has shifted at multidecadal timescale, opening the rooms for further research. This work informs the potential of a systematic causal approach to statistical inference as a powerful tool to verify and explore atmospheric teleconnections and enables seasonal forecasting to strengthen prevention and control of drought and fire multihazards over peatlands in the study region.
Keywords: Tropical teleconnections, Causal inference, Climate variability, Drought, Indonesia
How to cite: Lam, T., Kretschmer, M., Adams, S., Arribas, A., Prudden, R., Saggioro, E., Catto, J., and Barciela, R.: Quantifying Teleconnection pathways leading to Low Rainfall anomalies during Boreal Summer in Indonesian Borneo, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13694, https://doi.org/10.5194/egusphere-egu21-13694, 2021.
We investigate the seasonal predictability of the two dominant atmospheric teleconnections associated with the North Atlantic Jet: the Summer North Atlantic Oscillation (SNAO) and East Atlantic Pattern (EAP). We go beyond standard forecast practices by combining an ensemble predictions system with a machine learning approach. Specifically, we combine on the one hand a 30-member hindcast ensemble initialised every May between 1902 and 2008 in the Max Planck Institute Earth System Model in mixed resolution (MPI-ESM-MR), with on the other hand a neural network-based classifier Self-Organising Maps (SOM) in the ERA-20C reanalysis. We use the SOM to identify a sub-ensemble in which simulated North Atlantic sea surface temperatures (SST) at the initialisation of the prediction system (i.e. April) are linked to atmospheric modes.
While we find for summer climate at 3-4 months lead time only limited predictive skill in the ensemble mean of MPI-ESM-MR, we find significant predictive skill over many areas in the SOM-based sub-ensemble. Our results suggest that the predictive skill of European summer temperatures can be linked to the predictive skill of SNAO and EAP, which stems in turn from the – with skill predictable - temperature gradient between subpolar and subtropical gyres. We also demonstrate the predictive skill is time dependent, with high skill over the late half of the time series (1955 - 2008) and low skill in the early period (1902 - 1954).
How to cite: Carvalho Oliveira, J., Borchert, L., Koul, V., Baehr, J., and Zorita, E.: Role of SST for the predictability of summer atmospheric teleconnections in the Euro-Atlantic region with Self-Organising Maps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12575, https://doi.org/10.5194/egusphere-egu21-12575, 2021.
In order to predict the timing of extreme summer seasons in terms of 2 meters surface temperature (t2m), two algorithms are applied in the output of a global paleoclimate simulation. The model simulation is conducted with the Max Planck Institute Earth System Model configuration for paleoclimate (MPI-ESM-P). Global model output is provided for the period 0–1999 AD on a horizontal resolution of approximately 187 km (1.875°× 1.875°longitude by latitude grid). The t2m extremes are defined as the events with mean summer temperature higher than the 95th percentile of the training period (0-1970 AD) and are calculated separately for each grid point of the European region between 35ºN-70ºN, 10ºW-30ºE. The algorithms are trained only with data from the training period and are set to predict the summer t2m extremes of the test period 1971-1999 AD. The predictor data used for fitting the algorithms are chosen based on their known influence as boundary forcings of European summer climate. The predictor variables include springtime sea surface temperature (SST) from the NA region (0º-76ºN, 85°W-30°E) and springtime European soil moisture (SM). The skill of the predictions is evaluated based on the extremal dependence index (EDI), which depends on the hit rate and false alarm rate. The EDI values vary between -1 and 1, where 1 is the skill of a perfect forecast. The first algorithm tested is a supervised learning algorithm, which is based on a random forest classifier (RF). RF predicts the highest EDI values over Scandinavia, Scotland and around the Mediterranean region (EDI>0.5), with the SST predictor being the main contributor to that skill. The second algorithm tested, is an autoencoder neural network (AE) that learns data codings in an unsupervised manner. AE surpasses the RF skill above most European regions and predicts the highest EDI values over the southeast Mediterranean, Central Europe, and the British Isles. The AE neural network is also trained to predict the absolute value of the extreme t2m events. The skill of reproducing the absolute value of the target t2m extremes is evaluated with the Mean Absolute Error (MAE), only for those extreme events that are reproduced by the AE prediction. The MAE values for the southeast Mediterranean region, Central Europe, and the British Isles are around 2 ºC, 2.5 ºC, and 1.5 ºC, respectively. We have demonstrated the possibility of predicting a season in advance the occurrence of extreme summer t2m using an AE neural network. The AE neural network was tested in the virtual reality of a model simulation. The second step will be the application of the trained network on observational data.
How to cite: Pyrina, M., Zorita, E., and Wagner, S.: Supervised and unsupervised learning algorithms for extreme summer temperature prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12439, https://doi.org/10.5194/egusphere-egu21-12439, 2021.
DWD provides operational seasonal and decadal predictions of the German climate prediction system since 2016 and 2020, respectively. We plan to present these predictions together with post-processed ECMWF sub-seasonal forecast products on the DWD climate prediction website www.dwd.de/climatepredictions. In March 2020, this climate service was published with decadal predictions for the coming years; sub-seasonal and seasonal predictions for the coming weeks and months will follow.
The user-oriented evaluation and design of this climate service has been developed in close cooperation with users from various sectors at workshops of the German MiKlip project and will be consistent across all time scales. The website offers maps, time series and tables of ensemble mean and probabilistic predictions in combination with the prediction skill for 1-year and 5-year means/ sums of temperature and precipitation for different regions (World, Europe, Germany, German regions).
For Germany, the statistical downscaling EPISODES was applied to reach high spatial resolution required by several climate data users. Decadal predictions were statistically recalibrated in order to adjust bias, drift and standard deviation and optimize ensemble spread. We used the MSESS and RPSS to evaluate the skill of climate predictions in comparison to reference predictions, e.g. ‘observed climatology’ or ‘uninitialized climate projections’ (which are both applied by users until now as an alternative to climate predictions). The significance was tested via bootstraps.
Within the ‘basic climate predictions’ section, a user-oriented traffic light indicates whether regional-mean climate predictions are significantly better (green), not significantly different (yellow) or significantly worse (red) than reference predictions. Within the ‘expert climate predictions’ section, prediction maps show per grid box the prediction itself (via the color of dots) and its skill (via the size of dots representing the skill categories of the traffic light). The co-development of this climate prediction application with users from different sectors strongly improves the comprehensibility and applicability by users in their daily work.
In addition to sub-seasonal and seasonal predictions, plans for future extensions of this climate service include multi-year seasonal predictions, e.g. 5-year summer or winter means, combined products for climate predictions and climate projections, further user-oriented, extreme or large-scale variables, e.g. ENSO, or high-resolution applications for German cities based on statistically downscaled predictions.
How to cite: Paxian, A., Reinhardt, K., Mannig, B., Isensee, K., Krug, A., Pankatz, K., Fröhlich, K., Tivig, M., Lorenz, P., and Früh, B.: The DWD climate prediction website, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3236, https://doi.org/10.5194/egusphere-egu21-3236, 2021.
Here we present an overview of results emerging from a project to develop prototype decadal climate prediction services, funded by the EU Copernicus Climate Change Service (C3S). The field of interannual to decadal climate prediction has matured rapidly over the last ~15 years, becoming an established part of the Coupled Model Intercomparison Project (CMIP) process with multi-model decadal climate predictions made in CMIP5 and CMIP6 (DCPP MIP). It has further been highlighted by the recent creation of the WMO Lead Centre for Annual-to-Decadal Climate Prediction. Whilst these activities have led to rapid development in our understanding of decadal climate predictability and mechanisms driving global and regional annual to decadal climate variability, the creation of useful climate services on this timescale is still in its infancy.
This EU funded project was designed to start to address decadal climate services and brings together many of the key European institutions involved in decadal climate predictions from four different countries: Germany (DWD), Italy (CMCC), Spain (BSC) and the UK (Met Office). Each partner is working with a different sector: infrastructure, energy, agriculture and insurance where they have been developing a prototype decadal climate service in partnership with a user in that sector. Here we report on the progress made so far and highlight a number of key lessons learned along the way. These include the use of both large multi-model ensembles and more predictable large-scale circulation indicators in order to give skilful regional predictions of user relevant variables. We also describe the development of a common product format to present forecast information to users, this contains essential information about the current probabilistic forecast, retrospective forecast skill and reliability.
How to cite: Dunstone, N., Athanasiadis, P., Caron, L.-P., Doblas-Reyes, F., Frueh, B., Hermanson, L., Lockwood, J., Pankatz, K., Paxian, A., Reinhardt, K., Scaife, A., Smith, D., Solaraju, B., Thornton, H., and Tsartsali, E.: Developing prototype decadal climate prediction services, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5437, https://doi.org/10.5194/egusphere-egu21-5437, 2021.
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