CL4.3
Orals: Fri, 28 Apr | Room 0.49/50
Decadal prediction represents a source of near-term climate information that has the potential to support climate-related decisions in key socio-economic sectors that are influenced by climate variability and change. While the research to illustrate the ability of decadal predictions in forecasting the varying climate conditions on a multi-annual timescale is rapidly evolving, the development of climate services based on such forecasts is still in its early stages. This study showcases the potential value of decadal predictions in the development of climate services. We summarize the lessons learnt from coproducing a forecast product that provides tailored and user-friendly information about multi-year drought conditions for the coming five years over global wheat harvesting regions. The interaction between the user and climate service provider that was established at an early stage and lasted throughout the forecast product development process proved fundamental to provide useful and ultimately actionable information to the stakeholders concerned with food production and security. This study also provides insights on the potential reasons behind the delayed entry of decadal predictions in the climate services discourse and practice, which were obtained from surveying climate scientists and discussing with decadal prediction experts.
How to cite: Solaraju-Murali, B., Bojovic, D., Gonzalez-Reviriego, N., Nicodemou, A., Terrado, M., Caron, L.-P., and Doblas-Reyes, F. J.: Better late than never: arrival of decadal predictions to the climate services arena, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8296, https://doi.org/10.5194/egusphere-egu23-8296, 2023.
Current global climate models typically fail to fully resolve mesoscale ocean features (with length scales on the order of 10 km), such as the western boundary currents, potentially limiting climate predictability over decadal timescales. This study incorporates high-resolution eddy-resolving ocean (HR: 0.1°) in a suite of CESM model experiments that capture these important mesoscale ocean features with increased fidelity. Compared with eddy-parametrized ocean (LR: 1°) experiments, HR experiments show more realistic climatology and variability of sea surface temperature (SST) over the western boundary currents and eddy-rich regions. In the North Atlantic, the inclusion of mesoscale ocean processes produces a more realistic Gulf Stream and improves both localized rainfall patterns and large-scale teleconnections. We identify enhanced decadal SST predictability in HR over the western North Atlantic, which can be explained by the strong vertical connectivity between SST and sub-surface ocean temperature. SST is better connected with slower processes deep down in HR, making SST more persistent (and predictable). Moreover, we detect a better representation of the air-sea interactions between SST and low-level atmosphere over the Gulf Stream, thus improving low-frequency rainfall variations and extremes over the Southeast US. The results further imply that high-resolution GCMs with increased ocean model resolution may be needed in future climate prediction systems.
How to cite: Zhang, W., Kirtman, B., Siqueira, L., and Clement, A.: Decadal Climate Variability and Predictability with a High-resolution Eddy-resolving Model , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7676, https://doi.org/10.5194/egusphere-egu23-7676, 2023.
We introduce a simple data assimilation approach applied to the coupled global climate model EC-Earth3.3.1, aiming at producing initial conditions for decadal climate hindcasts and forecasts. We rely on a small selection of assimilated variables, which are available in a consistent manner for a long period, providing good spatial coverage for large parts of the globe, that is sea-surface temperatures (SST) and near-surface winds.
Given that these variables play a role directly at or very close to the ocean-atmosphere interface, we assume a comparably strong cross-component impact of the data assimilation. Starting from five different free-running CMIP6-historical simulations in 1900, we first apply surface restoring in the model’s ocean component towards monthly means of HadISST1. After integrating this five-member ensemble with only assimilating SST for the period 1900-1949, we start additionally assimilating (nudging) 6-hourly near-surface winds (vorticity and divergence) taken from the ERA5 reanalysis from 1950 onwards. To mitigate the risk of model drifts after initializing the decadal predictions and to account for known instationary biases of the model, we assimilate anomalies of all variables that are calculated based on a 30-year running mean.
By assimilating near-surface data over several decades before entering the actual period targeted by the decadal hindcasts/forecasts for CMIP6-DCPP, we expect the coupled model to be able to ingest a significant share of observed climate evolution also in deeper ocean layers. This would then potentially serve as a source of predictive skill on interannual-to-decadal timescales.
We show that the presented assimilation approach is able to force the coupled model’s evolution well in phase with observed climate variability, positively affecting not only near-surface levels of the atmosphere and ocean but also deeper layers of the ocean and higher levels of the atmosphere as well as Arctic sea-ice variability. However, we also present certain problematic features of our approach. Two examples are significantly strengthened low-frequency variability of the AMOC and a wind bias resulting into generally reduced evaporation over ocean areas.
How to cite: Kruschke, T., Karami, M. P., Docquier, D., Schenk, F., Fuentes Franco, R., Willén, U., Wang, S., Wyser, K., Fladrich, U., and Koenigk, T.: A simple coupled assimilation approach for improved initialization of decadal climate predictions, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8750, https://doi.org/10.5194/egusphere-egu23-8750, 2023.
A considerable part of the skill in decadal forecasts often come from the forcings which are present in both initialized and un-initialized model experiments. This makes the added value from initialization difficult to assess. We investigate statistical tests to quantify if initialized forecasts provide skill over the un-initialized experiments. We consider three correlation based statistics previous used in the literature. The distributions of these statistics under the null-hypothesis that initialization has no added values are calculated by a surrogate data method. We present some simple examples and study the statistical power of the tests. We find that there can be large differences in both the values and the power for the different statistics. In general the simple statistic defined as the difference between the skill of the initialized and uninitialized experiments behaves best. However, for all statistics the risk of rejecting the true null-hypothesis is too high compared to the nominal value.
We compare the three tests on initialized decadal predictions (hindcasts) of near-surface temperature performed with a climate model and find evidence for a significant effect of initializations for small lead-times. In contrast, we find only little evidence for a significant effect of initializations for lead-times larger than 3 years when the experience from the simple experiments is included in the estimation.
How to cite: Christiansen, B., Yang, S., and Matte, D.: Estimating the significance of the added skill from initializations: The case of decadal predictions, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9520, https://doi.org/10.5194/egusphere-egu23-9520, 2023.
How to cite: Drews, A., Schmith, T., Yang, S., Olsen, S., Tian, T., Devilliers, M., Wang, Y., and Keenlyside, N.: Role of the subpolar North Atlantic region in skillful climate predictions for high northern latitudes: A pacemaker experiment, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13375, https://doi.org/10.5194/egusphere-egu23-13375, 2023.
Climate extremes can impact societies in various ways: from nuances in daily lives to full humanitarian crises. Droughts are usually slow onset extremes but can be highly disruptive and affect millions of people every year. Warm temperature extremes (e.g. heat waves) can exacerbate droughts and their impacts and trigger a faster drought evolution. Combined drought and heat waves can lead to devastating consequences. For example, 2022 was a very active year in terms of drought or combined drought and heat waves, affecting particularly hard several regions of the world (e.g. Europe, China, southern South America and East Africa). In a context of risk management and civil protection, the use of operationally available seasonal climate forecasts can provide actionable information to reduce the risks and the impacts of these events on societies with different levels of development and adaptive capacities.
Within the Copernicus Emergency Management Service (CEMS), the European and Global Drought Observatories (EDO and GDO, respectively) provide real time drought and temperature extreme monitoring products freely available and displayed through two dedicated web services. Recent efforts have been targeting the optimal integration and use of multi-system forecasting products to enhance the early warning component of the service. This contribution provides an overview of first results in terms of initial multi-model skill assessment of forecasts available through the Copernicus Climate Change Service (C3S). It also discusses future avenues to improve skill in regions with limited predictability, for example by applying physically-based sampling techniques.
How to cite: Acosta Navarro, J. C. and Toreti, A.: Seasonal forecasting of drought and temperature extremes as part of the Copernicus Emergency Management Service (CEMS), EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5602, https://doi.org/10.5194/egusphere-egu23-5602, 2023.
The European North West shelf seas (NWS) support economic and environmental interests of several adjacent populous countries. Forecasts of physical marine variables on the NWS for upcoming months – an important decision-making timescale – would be useful for many industries. However, currently there is no operational seasonal forecasting product deemed sufficient for capturing the high variability associated with shallow, dynamic shelf waters. Here, we identify the dominant sources of seasonal predictability on the shelf and quantify the extent to which empirical persistence relationships can produce skilful seasonal forecasts of the NWS at the lowest level complexity. We find that relatively skilful forecasts of the typically well-mixed Winter and Spring seasons are achievable via persistence methods at a one-month lead time. In addition, incorporating observed climate modes of variability, such as the North Atlantic Oscillation (NAO), can significantly boost persistence for some locations and seasons, but this is dependent on the strength of the climate mode index. However, even where high persistence skill is demonstrated, there are sizeable regions exhibiting poor predictability and skilful persistence forecasts are typically limited to ≈ one-month lead times. Summer and Autumn forecasts are generally less skilful owing largely to the effects of seasonal stratification which emphasises the influence of atmospheric variability on sea surface conditions. As such, we also begin incorporating knowledge of future atmospheric conditions to forecasting strategies. We assess the ability of an existing global coupled ocean-atmosphere seasonal forecasting system to exceed persistence skill and highlight areas where additional downscaling efforts may be needed.
How to cite: Atkins, J., Tinker, J., Graham, J., Scaife, A., and Halloran, P.: Seasonal forecasting of the European North West Shelf: Quantifying the persistence of the physical marine environment, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6000, https://doi.org/10.5194/egusphere-egu23-6000, 2023.
For several years the Met Office has produced a seasonal outlook for the UK every month, which is issued to the UK Government and contingency planners. The outlook gives predictions of the probability of having average, low, or high seasonal mean UK temperature and precipitation for the coming three-months. In recent years, there has been increasing demand from sectors such as energy and insurance to include similar probabilistic predictions of UK wind speed: both for the seasonal mean and for measures of extreme winds such as storm numbers. In this presentation we show the skill of the Met Office’s GloSea system in predicting seasonal (three-month average) UK mean wind and a measure of UK storminess throughout the year, and discuss the drivers of predictability. Skill in predicting the UK mean wind speed and storminess peaks in winter (December–February), owing to predictability of the North Atlantic oscillation. In summer (June–August), there is evidence that a significant proportion of variability in UK winds is driven by a Rossby wave train which the model has little skill in predicting. Nevertheless there are signs that the wave is potentially predictable and skill may be improved by reducing model errors.
How to cite: Lockwood, J., Stringer, N., Hodge, K., Bett, P., Knight, J., Smith, D., Scaife, A., Patterson, M., Dunstone, N., and Thornton, H.: Seasonal prediction of UK mean and extreme winds, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13639, https://doi.org/10.5194/egusphere-egu23-13639, 2023.
Earth system predictability on decadal timescales can arise from both low frequency internal variability as well as from anthropogenically forced long-term changes. However, on these timescales, the chaotic nature of the climate system makes skillful predictions difficult to achieve even if we include information from climate change projections. Furthermore, it is difficult to separate the contributions from internal variability and external forcing to predictability. One way to improve skill is through identifying and harnessing initial conditions with more predictable evolution, so-called state-dependent predictability. We explore a neural network approach to identify these opportunistic initial states in the CESM2 large ensemble and subsequently explore how predictability may manifest in a future climate, influenced by both forced warming and internal variability. We use an interpretable neural network to demonstrate that internal variability will continue to play an important role in future climate predictions, especially for states of increased predictability.
How to cite: Gordon, E. and Barnes, E.: An interpretable neural network approach to identifying sources of predictability in the future climate, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8000, https://doi.org/10.5194/egusphere-egu23-8000, 2023.
The state-of-the-art climate models suffer from significant sea surface temperature (SST) biases in the tropical Indian Ocean (TIO), greatly damaging the climate prediction and projection. In this study, we investigate the multidecadal atmospheric bias teleconnections caused by the TIO SST biases and their impacts on the simulated atmospheric variability. A set of century long simulations forced with idealized SST perturbations, resembling various persistent TIO SST biases in coupled climate models, are conducted with an intermediate complexity climate model. Bias analysis is performed using the normal-mode function decomposition which can differentiate between balanced and unbalanced flow regimes across spatial scales. The results show that the long-term atmospheric circulation biases caused by the TIO SST biases have the Matsuno-Gill-type pattern in the tropics and Rossby wavetrain distribution in the extratropics, similar to the steady state response to tropical heating. The teleconnection between the tropical and extratropical biases is set up by the Rossby wavetrain emanating from the subtropics. Over 90% of the total bias energy is stored in the zonal modes k≤6, and the Kelvin modes take 50-65% of the total unbalanced bias energy. The spatial and temporal variabilities have different responses to positive SST biases. In the unbalanced regime, variability changes are confined in the tropics, but the spatial variability increases whereas the temporal variability decreases. In the balanced regime, the spatial variability generally increases in the tropics and decreases in the extratropics, whereas the temporal variability decreases globally. Variability responses in the tropics are confined in the Indo-west Pacific region, and those in the extratropics are strong in the Pacific-North America region and the Europe. In the experiment with only negative SST biases, spatial and temporal variabilities increase in both regimes. In addition, the comparison between experiments indicates that the responses of the circulation and its variability are not sensitive to the structure and location of the TIO SST biases.
How to cite: Zhao, Y.-B., Žagar, N., Lunkeit, F., and Blender, R.: Long-term atmospheric bias teleconnection and the associated spatio-temporal variability originating from the tropical Indian Ocean sea surface temperature errors, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16899, https://doi.org/10.5194/egusphere-egu23-16899, 2023.
Posters on site: Fri, 28 Apr, 14:00–15:45 | Hall X5
Earth system models are now regularly being used in inter-annual to decadal climate prediction. Such prediction systems based on CMIP5-generation Earth system models had demonstrated an overall positive impact of initialization, i.e. deriving initial conditions of retrospective forecasts from a separate data assimilation experiment, on decadal prediction skill. This view is now being increasingly challenged in the context of improvements both in CMIP6-generation Earth system models and CMIP6-evaluation of external forcing as well as in the context of ongoing transient climate change. In this study we re-evaluate the impact of atmospheric and oceanic initialization on decadal prediction skill of North Atlantic upper ocean heat content (0-700m) in a CMIP6-generation decadal prediction system based on the Max Planck Institute Earth system model (MPI-ESM). We compare the impact of initial conditions derived through full-field atmospheric nudging with those derived through an additional assimilation of observed oceanic temperature and salinity profiles using an ensemble Kalman filter. Our experiments suggest that assimilation of observed oceanic temperature and salinity profiles into the model reduces the warm bias in the subpolar North Atlantic heat content, and improves the modelled variability of the Atlantic meridional overturning circulation and ocean heat transport. These improvements enable a proper initialization of model variables which leads to an improved decadal prediction of surface temperatures. Our results reveal an important role of subsurface oceanic observations in decadal prediction of surface temperatures in the subpolar North Atlantic even in CMIP6-generation decadal prediction system.
How to cite: Brune, S., Koul, V., and Baehr, J.: Do oceanic observations (still) matter in initializing decadal climate predictions over the North Atlantic ocean?, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13789, https://doi.org/10.5194/egusphere-egu23-13789, 2023.
Accurate predictions of climate variations at the decadal timescale are of great interest for decision-making, planning and adaptation strategies for different socio-economic sectors. Notably, decadal predictions have rapidly evolved during the last 15 years and are now produced operationally worldwide. The majority of the studies assessing the skill of decadal prediction systems focus on time-mean anomalies of standard meteorological variables, such as annual mean near-surface air temperature and precipitation. However, the predictability of extreme events frequency may differ substantially from the predictability of multi-year annual or seasonal means. Predicting the frequency of extreme events at different timescales is of major importance, since they are associated with severe impacts on various natural and human systems. In the current study we evaluate the capability of state-of-the-art decadal prediction systems to predict the frequency of temperature extremes in Europe. More specifically, we assess the skill of a multi-model ensemble from the Decadal Climate Prediction Project (DCPP, 163 ensemble members from 12 models in total) to forecast the number of days belonging to heatwaves episodes during summer (June–August). We find statistically significant predictive skill over Europe, except for the United Kingdom and a large part of the Scandinavian Peninsula, most of which is associated with the long-term warming trend. We are progressing with the evaluation of other statistical aspects of extreme events, including warm and cold episodes during winter, and we are also investigating whether there is predictive skill beyond that stemming from the external forcing.
How to cite: Tsartsali, E., Athanasiadis, P., Tibaldi, S., and Gualdi, S.: Decadal predictability of European temperature extremes., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13736, https://doi.org/10.5194/egusphere-egu23-13736, 2023.
Ongoing climate change makes both short- and long-term adaptation and mitigation strategies urgently needed. While many long-term climate models have been developed and investigated in recent years, little attention has been paid to short-term simulations. The first attempts to perform multi-model initialized decadal forecasts were presented in the fifth Coupled Model Intercomparison Project 5 (CMIP5). Near-term climate prediction models are new socially relevant tools to support the decision makers delivering climate adaptation solutions on an annual or decadal scale. Recent improvements in decadal models were coordinated in CMIP6 and the World Climate Research Program (WCRP) Grand Challenge on Near Term Climate Prediction, as part of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC). The Decadal Climate Prediction Project (DCPP) provides decadal climate forecasts based on advanced techniques for the reanalysis of climate data, initialization methods, ensemble generation and data analysis. The initialization allows to consider the predictability of the internal climate variability reducing the prediction errors compared to those of the long-term projections, whose simulations do not take into account the phasing between the internal variability of the model and the observations. The aim of this work is to assess the near-future climate change in the Emilia-Romagna region in northern Italy until 2031. The hydrological variables analyzed are the daily precipitation and maximum and minimum temperature. An ensemble of models, with the highest resolution available, is used to handle the uncertainty in the predictions. Initially, to assess the reliability of the selected climate models, the hindcast data of the DCPP are checked against observations. Then, the DCPP predictions are used to investigate the variability of precipitation and temperature in the near future over the investigated area. Some climate features that are referenced to have an important impact on human health and activities are evaluated, such as drought indices and heat waves.
How to cite: Todaro, V., D'Oria, M., Secci, D., Zanini, A., and Tanda, M. G.: Near term climate change in Emilia-Romagna (Italy) using CMIP6 decadal climate predictions, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15829, https://doi.org/10.5194/egusphere-egu23-15829, 2023.
The polar vortex in the wintertime Northern Hemisphere can sometimes experience a dramatic breakdown after an associated warming of the stratosphere during so-called Sudden Stratospheric Warmings (SSWs). These events are known to influence the ground weather in Northern Eurasia and large parts of North America. SSWs are primarily generated by enhanced planetary waves propagating from the troposphere to the stratosphere where they decelerate the vortex and lead to its breakdown. According to the Holton-Tan mechanism, the easterly direction of equatorial stratospheric QBO (Quasi-Biennial Oscillation) winds weakens the northern polar vortex by guiding more waves poleward. Recently, we found that during easterly QBO the occurrence rate of SSWs is modulated by the geomagnetic activity. We used the aa-index which is a good proxy for the energetic electron precipitations (EEP) responsible for the indirect effect on ozone. Our model shows that the breaking of the polar vortex is very likely to occur if the geomagnetic activity is weak. On the other hand, during westerly QBO, solar irradiance modulates the SSW occurrence: more SSWs happen under high solar activity.
How to cite: Vokhmyanin, M., Asikainen, T., Salminen, A., and Mursula, K.: Seasonal forecast of the Sudden Stratospheric Warming occurrence, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16034, https://doi.org/10.5194/egusphere-egu23-16034, 2023.
The monthly anomaly sea surface temperature field over the global ocean exhibit probabilistic dependencies between remote points and lagged times, which are explained eventually by some oceanic or atmospheric bridge of information transfer. Despite much of the bivariate SST dependencies appear to be linear, others are characterized by robust and statistically significant nonlinear correlations. In order to enhance that, we present a general method of extracting bivariate (X,Y) dependencies, seeking for pairs of polynomials P(X) and Q(Y) which are maximally correlated. The method relies on a Canonical correlation Analysis (CCA) between sets of standardized monomials of X and Y, up to a certain (low) degree (e.g. 4). Polynomial coefficients are obtained from the leading CCA eigenvector. Polynomials are calibrated and validated over independent periods, being afterwards subjected to marginal Gaussian anamorphoses. The bivariate non-Gaussianity in the space of marginally Gaussianized polynomials remains residual because of the correlation concentration and maximization. Consequently, the bivariate Gaussian pdf or in alternative, a copula pdf in the space of maximally correlated polynomials can accurately approximate the bivariate dependency. That probabilistic model is then used to determine conditional pdfs, cdfs and probabilities of extremes.
The method is applied to various (X,Y) pairs. In the first example, X is an optimized polynomial of the El-Niño 3.4 index while Y is that index lagged to the future. For lags between 6 and 18 months, the nonlinear El-Niño forecast clearly surpasses the linear one, contributing to lower the El-Niño seasonal predictability barrier. In the second example, we relate El-Niño (X) with the lagged Atlantic multidecadal oscillation index (Y). Nonlinear, robust correlations appear, both for positive and negative lags up to 5 years putting in evidence Pacific-Atlantic basin oceanic teleconnections.
The above probabilistic (polynomial based) model appears to be a good candidate tool for the statistical (seasonal up to decadal) forecast of regime probabilities (e.g. dry/wet) and extremes, given certain antecedent precursors.
This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL and the project JPIOCEANS/0001/2019 (ROADMAP: ’The Role of ocean dynamics and Ocean–Atmosphere interactions in Driving cliMAte variations and future Projections of impact–relevant extreme events’). Acknowledgements are also due to the International Meteorological Institute (IMI) at Stockholm University.
How to cite: Pires, C. and Hannachi, A.: Probabilistic nonlinear lagged teleconnections of the sea surface temperature field, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9986, https://doi.org/10.5194/egusphere-egu23-9986, 2023.
The global climatic pattern is governed by the dominant mode of variability in the tropics and the extratropic and their interaction. The extratropical atmosphere is much more vigorous than the tropics owing to sharp meridional temperature gradients in the mid-latitude. Especially on the decadal timescales, large signals are seen over the extratropical region than in the tropics. Here, we propose that during boreal spring, the second leading mode of climate variability in the Southern Hemisphere, has a decadal pattern. This mode is independent of the Southern Annular Mode (SAM), which represents the most dominant mode of climate variability in the Southern Hemisphere. The boreal spring climate of the Southern Hemisphere interacts with the tropics and significantly impacts the global climate, which is reflected in the global Monsoon rainfall. During the positive phase of the decadal mode, the global Monsoon rainfall is coherently suppressed. We propose a new finding highlighting that the Southern Hemisphere's extratropical forcing can significantly impact the tropical Pacific through subtropical pathways on the decadal to multidecadal timescale. The impact of such decadal climate variability is enormous and global and can add a new paradigm to the pursuit of improving decadal predictions of the global climate.
How to cite: Srivastava, S., Chakraborty, A., and Murtugudde, R.: Boreal Spring Southern Hemisphere Climate Mode and Global Monsoon, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-604, https://doi.org/10.5194/egusphere-egu23-604, 2023.
Earth System Models (ESMs) are complex, highly nonlinear, multi-component systems described by large number of differential equations. They are used to study the evolution of climate and its dynamics, and to make projection of future climate at both regional and global levels – which underpins climate change impact assessments such as the IPCC report. These projections are subject to several sorts of uncertainty due to high internal variability in the system dynamics, which are usually quantified via ensembles of simulations.
Due to their multi component nature of such ESMs, the emerging dynamics also contain different temporal scales, meaning that climate ensembles come in a variety of shapes and sizes. However, our ability to run such ensembles is usually constrained by the computational resources available, as they are very expensive to run. Hence, choices on the ensemble design must be made, which conciliate the computational capability with the sort of information one is looking for.
One alternative to gain information is to use low-dimensional climate-like systems, which consists of simplified, coupled versions of atmosphere, ocean, and other components, and hence capture some of the different time scales present in ESMs. This approach allows one to run very large ensembles, and hence to explore all sorts of model uncertainty with only modest computational usage.
In this talk, we discuss this approach in detail, and illustrate its applicability with a few results. Particular attention will be given to the issues of micro and macro initial condition uncertainty, and parametric uncertainty – including external, anthropogenic-like forcing. The ability of large ensembles to constrain decadal to centennial projections will be also explored.
How to cite: de Melo Viríssimo, F. and Stainforth, D.: A low-dimensional dynamical systems approach to climate ensemble design and interpretation, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14755, https://doi.org/10.5194/egusphere-egu23-14755, 2023.
As a dominant pattern of the North Pacific sea surface temperature decadal variability, the Pacific Decadal Oscillation (PDO) has remarkable influences on the marine and terrestrial ecosystems. However, the PDO is highly unpredictable. Here, we assess the performance of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the PDO, with an emphasis on the evaluation of CMIP6 models in reproducing a recently detected early warning signal based on climate network analysis for the PDO regime shift. Results show that the skill of CMIP6 historical simulations remains at a low level, with a skill limited in reproducing PDO’s spatial pattern and nearly no skill in reproducing the PDO index. However, if the warning signal for the PDO regime shift by climate network analysis is considered as a test-bed, we find that even in historical simulations, a few models can represent the corresponding relationship between the warning signal and the PDO regime shift, regardless of the chronological accuracy. By further conducting initialization, the performance of the model simulations is improved according to the evaluation of the hindcasts from two ensemble members of the Decadal Climate Prediction Project (NorCPM1 and BCC-CSM2-MR). Particularly, we find that the NorCPM1 model can capture the early warning signals for the late-1970s and late-1990s regime shifts 5–7 years in advance, indicating that the early warning sig- nal somewhat can be captured by some CMIP6 models. A further investigation on the underlying mechanisms of the early warning signal would be crucial for the improvement of model simulations in the North Pacific.
How to cite: Ma, Y.: On the Pacific Decadal Oscillation Simulations in CMIP6 Models: A New Test‐Bed from Climate Network Analysis , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5325, https://doi.org/10.5194/egusphere-egu23-5325, 2023.
The interdisciplinary research project "BayTreeNet" examines the reactions of forest ecosystems to climate dynamics. To establish a relationship between tree growth and climate, it is important to know that in the mid-latitudes, local climate phenomena often show a strong dependence on the large-scale climate weather types (WT), which significantly determine the climate of a region through frequency and intensity. Different WT show various weather conditions at different locations, especially in the topographically diverse region of Bavaria. The meaning of every WT is the physical basis for the climate-growth relationships established in the dendroecology sub-project to investigate the response of forests to individual WT at different forest sites. Complementary steps allow interpretation of results for the past (20th century) and projection into the future (21st century). One hypothesis is that forest sites in Bavaria are affected by a significant influence of climate change in the 21st century and the associated change in WT.
The automated classification of large-scale weather patterns is presented by Self-Organizing-Maps (SOM) developed by Kohonen, which enables visualization and reduction of high-dimensional data. The poster presents the SOM-setting which was used to classify the WT and the results of past environmental conditions (1990-2019) for different WT in Europe based on ERA5 data. Morover, it shows a future projection until 2100 for European WT and their respective environmental conditions. The projections are based on a novel GCM selection technique for two scenarios (ssp1-2.6 and ssp5-8.5) to obtain a range of the most likely conditions.
How to cite: Wehrmann, S. and Mölg, T.: GCM-based future projections of European weather types obtained by Self‑Organizing-Maps and a novel GCM selection technique, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8934, https://doi.org/10.5194/egusphere-egu23-8934, 2023.
