Several single model large ensemble simulations from Global Climate Models (GCM), Earth System Models (ESM), or Regional Climate Models (RCM), have been generated over the recent years to investigate internal variability and forced changes of the climate system—and to aid the interpretation of the observational record by providing a range of historical climate trajectories that could have been. The increased availability of large ensembles also enables new and inter-disciplinary applications beyond large-scale climate dynamics.
This session invites studies using large GCM, ESM, or RCM ensembles looking at the following topics: 1) Reinterpretation of the observed record in light of internal variability; 2) forced changes in internal variability; 3) development of new approaches to attribute observed events or trends; 4) impacts of natural climate variability; 5) assessment of extreme and compound event occurrence; 6) combining single model large ensembles with CMIP archives for robust decision making; 7) large ensembles as testbeds for method development.
We welcome research across all components of the Earth system, including for example hydrology and biogeochemistry, but also research on the role of internal variability in impact studies focused for example on agriculture, air pollution or energy generation and consumption. We particularly invite studies that apply novel methods or cross-disciplinary approaches to leverage the potential of large ensembles.
vPICO presentations: Fri, 30 Apr
The large sample sizes from single-model large ensembles are beneficial for a robust attribution of climate changes to anthropogenic forcing. This presentation will review examples using large ensembles in two types of attribution: standard detection and attribution of spatio-temporal changes and extreme event attribution. First, increases in extreme precipitation have been attributed to anthropogenic forcing at large scales (global and hemispheric). We present results from a study that used three large ensembles, including two Earth System Models and one Regional Climate Model, to find a robust detection of a combined anthropogenic and natural forcing signal in the intensification of extreme precipitation at the continental scale and some regional scales in North America. Second, we use six large ensembles to assess the robustness of the attribution of extreme temperature and precipitation events. An event attribution framework is used and each large ensemble is treated as a perfect model. Robustness of the attribution is defined based on consistent agreement between the different models on a significant change in the probability of an event with the inclusion of anthropogenic forcing. We demonstrate that the attribution of extreme temperature events is robust. Meanwhile, the attribution of extreme precipitation events becomes robust in many regions under additional warming, but uncertainties pertaining to changes in atmospheric dynamics hinder attribution confidence in other regions. We also demonstrate that smaller ensembles bring larger uncertainty to event attribution.
How to cite: Kirchmeier-Young, M., Zhang, X., and Wan, H.: Climate change attribution with large ensembles, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3404, https://doi.org/10.5194/egusphere-egu21-3404, 2021.
We evaluate whether anthropogenic influence has affected the observed extreme sea surface temperature (SST), defined as discrete events of anomalously warm or cold ocean temperatures, over the last decades. To this end we utilize three large ensembles of coupled climate models and use two methods. The ﬁrst method analyzes the observed long-term spatiotemporal changes of extreme SST to detect the presence of a signal beyond changes solely due to natural (internal) variability and to attribute the detected changes to external climate drivers. The second method is based on single event attribution, which determines how an external forcing have changed the likelihood of high-impact extreme SST events, such as the north Atlantic cold blob, the northeast Pacific warm blob, Tasman Sea marine heatwave, etc. In this study we further combine observations and model simulations under present and future forcing to assess how internal variability and anthropogenic climate change modulate extreme SST events.
How to cite: Barkhordarian, A. and Baehr, J.: Attribution of high-impact extreme sea surface temperature events, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1739, https://doi.org/10.5194/egusphere-egu21-1739, 2021.
Arctic liquid freshwater (FW) storage has shown a large increase over the past decades, posing the question: Is the Arctic FW budget already showing clear signs of anthropogenic climate change, or are the observed changes the result of multi-decadal variability? Using large ensemble simulations from the Community Earth System model (CESM), we show that the observed change in liquid and solid Arctic FW storage is likely already driven by the changing climate. Generally, the emergence of forced changes in Arctic FW fluxes occurs earlier for oceanic fluxes than for atmospheric or land fluxes. Nares Strait liquid FW flux is the first to show emergence outside the range of background variability in the model, with this change potentially already occurring, followed by Davis Strait. Other FW fluxes have likely started to shift but have not yet emerged into a completely different regime. By re-sampling the model simulations, we find that the already changing nature of many FW budget terms over the short (~maximum 25 years) observational period can delay detection of shift and emergence from observations. Future emissions reductions have the potential to avoid the emergence of some FW fluxes beyond the background variability, in particular for runoff and Fram Strait solid FW export. However, under both low and high warming scenarios, all FW fluxes show changes, just not always completely outside the background variability as simulated by the CESM. Overall, this study provides an example of how large ensembles can be used to diagnose forced changes in short observational timeseries.
How to cite: Jahn, A. and Laiho, R.: Forced Changes in the Arctic Freshwater Budget Emerge in the Early 21st Century , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13255, https://doi.org/10.5194/egusphere-egu21-13255, 2021.
Beside means, the forced response of the internal variability of the climate system is also of considerable practical interest. Teleconnections are one aspect of internal variability, and they derive their importance partly from their role in seasonal predictability. We compare the forced response of the ENSO-Indian monsoon teleconnection — as a first step of investigating the robustness of its modelling — in two Earth System Models, making use of the Large Ensemble data sets of the MPI-GE and CESM1-LE. We find considerable similarities of climatologies and the forced responses with respect to spatial patterns, in terms of e.g. MCA (Maximum Covariance Analaysis) modes. However, because of the mismatch of these patterns, both in terms of their weight and shape, the teleconnection associated with restricted areas, such as the domain of the so-called All-India Summer Monsoon Rainfall (AISMR) differ very considerably in the two models. While most representations of the teleconnection involving the principal modes of variability show a strengthening in the MPI-GE, not much change is detectable in the CEMS1-LE. In fact, the second modes, EOF2 or MCA2, are associated with much more change in the CESM1.
How to cite: Bodai, T. and Lee, J.-Y.: Robustness of modelling the forced change of the ENSO-Indian monsoon teleconnection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3904, https://doi.org/10.5194/egusphere-egu21-3904, 2021.
We study the forced response of the teleconnection between the El Niño–Southern Oscillation (ENSO) and the Indian summer monsoon (IM) in the Max Planck Institute Grand Ensemble, a set of Earth system ensemble simulations under historical and RCP forcing. The forced response of the teleconnection, or a characteristic of it, is defined as the time dependence of a correlation coefficient evaluated over the ensemble. We consider the temporal variability of spatial averages and that with respect to dominant spatial modes in the sense of Maximal Covariance Analysis, Canonical Correlation Analysis and Empirical Orthogonal Function analysis across the ensemble. A further representation of the teleconnection that we define here takes the point of view of the predictability of the complete spatiotemporal variability of the Indian summer monsoon. We find that the strengthening of the ENSO-IM teleconnection is robustly or consistently featured in view of various teleconnection representations, whether sea surface temperature (SST) or sea level pressure (SLP) is used to characterise ENSO, and both in the historical period and under the RCP8.5 forcing scenario. It is found to be associated dominantly with the principal mode of ENSO variability. Concerning representations that involve an autonomous characterisation of the Pacific, in terms of a linear regression model, the main contributor to the strengthening} is the regression coefficient, which can outcompete even a declining ENSO variability when it is represented by SLP. We also find that the forced change of the teleconnection is typically nonlinear by (1) formally rejecting the hypothesis that ergodicity holds, i.e., that expected values of temporal correlation coefficients with respect to the ensemble equal the ensemble-wise correlation coefficient itself, and also showing that (2) the trivial contributions of the forced changes in means and standard deviations are insignificant here. We also provide, in terms of the test statistics, global maps of the degree of nonlinearity/nonergodicity of the forced change of the teleconnection between local precipitation and ENSO.
How to cite: Drotos, G., Bodai, T., Ha, K.-J., Lee, J.-Y., and Chung, E.-S.: Nonlinear forced change and nonergodicity: The case of ENSO-Indian monsoon and global precipitation teleconnections, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7157, https://doi.org/10.5194/egusphere-egu21-7157, 2021.
The internal climate variability contributes to various aspects of climate change projections. This presentation will report results of the ensemble mean and spread of future projections of globally surface mean and extreme winds in boreal winter, based on single model initial-condition simulations forced by the SSP5-8.5 high-emissions scenario from a 50-member ensemble of CanESM5 models. Over the next half century, surface wind is projected to increase in the Northern Hemisphere mid-latitudes and increase in the Southern Hemisphere low-latitudes, an interhemispheric asymmetry feature relevant to large-scale changes in surface temperature and atmospheric circulation. Decreases in the surface extreme wind are clearer than the mean wind in the northern mid-latitudes. Large ensemble spreads are apparent in the mean and extreme wind changes, including spatial pattern and magnitude of the projected trends over the next half century. The internal climate variability generated components of the mean and extreme wind trends exhibit large-scale spatial coherences, and are comparable to the externally anthropogenic forced components of the trends.
How to cite: Yu, B., Zhang, X., Li, G., and Yu, W.: Interhemispheric asymmetry of surface mean and extreme wind projections in CanESM5 climate change simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-884, https://doi.org/10.5194/egusphere-egu21-884, 2021.
Seasonal drought has a serious impact on nature and human society, especially during vegetation growing periods. As climate change alters terrestrial hydrological cycle significantly, it is imperative to assess drought changes and develop corresponding risk management measures for adaptation. According to a series of warming targets proposed by IPCC, researchers have focused on the response of regional droughts to global warming, but with inconsistent conclusions due to the large uncertainties in soil moisture simulation by the climate models, and the difficulty in representing the internal variability of climate system by using multi-model ensemble, etc. As compared with Coupled Model Intercomparison Project Phase 5 (CMIP5) models, the future projection of soil moisture based on the latest CMIP6 shows opposite trends over parts of China. Therefore, we project seasonal soil drought over China by using the superensemble that includes a set of CMIP5 and CMIP6 soil moisture data, high resolution land surface simulations driven by bias-corrected CMIP5 climate forcings, as wells large ensemble (LE) simulation data. We also investigate the influences from internal variability, and model uncertainties in responding to global warming at different levels.
How to cite: Chen, S. and Yuan, X.: Multi-model superensemble projection of seasonal soil drought in the midst of various uncertainties, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9041, https://doi.org/10.5194/egusphere-egu21-9041, 2021.
The summer of 2018 was exceptionally warm and dry in western Europe. In the aftermath of such extreme weather events, questions arise on the role of climate change in the event and what future events might look like. We present physical storylines of similar future events to answer some of these questions. A storyline approach, focusing on physical processes and plausibility rather than probability, improves risk awareness through its relation with our memory of the observed event and contributes to decision making processes through their user focus. We select analogue events from large ensemble climate model simulations representing 2 °C and 3 °C global warming scenarios, and discuss how event severity, event drivers and physical processes are influenced by climate change. We show that future Rhine basin summer droughts like 2018 will be more severe. Decreased precipitation and increased potential evapotranspiration, caused by higher temperatures and increased incoming solar radiation, lead to higher precipitation deficits and lower plant available soil moisture. Possibly, changes in atmospheric circulation contribute to increased spring drought, amplifying the most severe summer drought events. The spatial extent of the most severe drought impacts increases substantially. The noted changes can partly be explained by changes in mean climate, but for many variables changes in the relative event severity on top of these mean changes contribute as well. Furthermore, the newly developed method is tested for robustness. It showcases that a balance, or compromise, is needed between analogue composite size and analogue extremity. Having a sufficiently large ensemble, such that robust analogues can be created for the observed event under consideration, is essential to provide reliable and robust climate change information.
How to cite: van der Wiel, K., Lenderink, G., and de Vries, H.: Physical storylines of future European drought events like 2018 based on ensemble climate modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1823, https://doi.org/10.5194/egusphere-egu21-1823, 2021.
Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.
How to cite: Elsworth, G., Lovenduski, N., and McKinnon, K.: Alternate history: A synthetic ensemble of ocean chlorophyll concentrations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1375, https://doi.org/10.5194/egusphere-egu21-1375, 2021.
The Multi-Model Large Ensemble Archive (MMLEA) is a collection of CMIP5-generation single model initial condition large ensembles (SMILEs) and thus provides estimates of internal variability from several independently developed coupled climate models. Work is underway to determine whether these simulations provide a range of historical regional climate variability suitable for statistically increasing the observed temperature sample. Alternative sequences of historical temperature can be constructed by combining a forced signal with estimates of internal climate noise; prior studies have used the forced response from one SMILE in concert with observational noise resampling to form an “observational large ensemble” (McKinnon et al. 2018). Analogous to a SMILE, an observational large ensemble can be used to statistically contextualize monthly to half-yearly extreme events, such as the persistently mild Siberian winter of 2020, and to develop additional extended hot or cold spell storylines to explore in future projections of regional climate.
In this study, an alternative approach to constructing an observational large ensemble of European surface air temperature over the historical period (1950-2014), made possible by the MMLEA, is explored. Rather than relying on forced response and internal variability, components not well-defined in the single realization of observed climate, the constructed circulation analogue method of dynamical adjustment is employed to separate temperature anomalies related to atmospheric circulation (“dynamic noise") from a more thermodynamically driven residual signal. The approach is advantageous because it can be applied in a similar manner to single realizations from both models and observations. Here, dynamic noise is computed by dividing each of the seven CMIP5-generation SMILEs in half and empirically estimating the component of temperature associated with interannual sea level pressure variability in one half of the SMILE using circulation analogues from members in the other half. Because ensemble means can be computed in SMILEs, it is possible to use the relationship between unforced temperature and unforced sea level pressure anomalies to construct dynamic noise. In observations, weekly-averaged analogues are assessed as a means to increase the size of the analogue pool such that the separation between dynamic noise and thermodynamic residual signal occurs in a manner more similar to that computed in the SMILEs.
The extent to which dynamic noise fields from different SMILEs are distinguishable from each other and from observational estimates is determined via spectral and spatial pattern analyses. To avoid introducing regional model bias into dynamic noise estimates, a mosaic approach will be taken; noise estimates from different models are mosaiced such that observed statistical properties are maintained at each grid point of the European domain. Upon validation, SMILE-derived dynamic noise and observational thermodynamic residual signal estimates are combined into a 50-member European observational large ensemble and evaluated via a multi-month extreme temperature frequency metric against the observational large ensemble developed by McKinnon et al. (2018). Anomalously persistent hot and cold spells found in the European observational large ensemble are further compared to events in out-of-sample future projections of climate from the CMIP6 archive.
How to cite: Merrifield, A. L., Lehner, F., Lorenz, R., and Knutti, R.: The Multi-Model Large Ensemble Archive as a climate noise generator: opportunities and outlooks for Observational Large Ensemble construction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8398, https://doi.org/10.5194/egusphere-egu21-8398, 2021.
We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.
How to cite: Suarez-Gutierrez, L., Milinski, S., and Maher, N.: Exploiting large ensembles for a better yet simpler climate model evaluation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2576, https://doi.org/10.5194/egusphere-egu21-2576, 2021.
The European summer heat wave of 2003 with record-breaking temperature anomalies was brought into connection with a blocking Omega circulation pattern, soil moisture deficit and high sea surface temperature, especially in the Mediterranean Sea. We investigate the potential factors influencing extreme heat waves in Europe with a very large ensemble obtained from multiple global integrations of the Model for Prediction Across Scales (MPAS). The global MPAS runs are performed in approximately 60 km resolution with sea surface temperature (SST) and sea ice extent from ERA-Interim data as boundary condition initialized on different days.
The contribution investigates the results obtained from a total of 540 simulations. It concentrates on the regional SST and weather patterns and moisture obtained in simulations contributing to the upper 10% of the resulting probability density function (PDF) of the summer daily mean and maximum temperature. The investigation considers in total eight standard evaluation domains in Europe as defined in the PRUDENCE project.
How to cite: Smiatek, G. and Kunstmann, H.: On the factors contributing to heat waves in Europe. A global large ensemble approach with MPAS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11831, https://doi.org/10.5194/egusphere-egu21-11831, 2021.
Large ensembles of climate model simulations may be used to assess the likelihood of extreme events, which only have a limited chance of occurring in observed records. In this talk, we discuss how the ECMWF seasonal prediction system SEAS5 can be used to generate a 100-member ensemble over 1981-present. SEAS5 is a global coupled ocean, sea-ice, atmosphere model with a horizontal resolution of 36 km. We introduce an open and reproducible workflow to retrieve Copernicus SEAS5 data and evaluate the ensemble member independence, model stability, and model fidelity. We illustrate how the increased sample size may help risk estimation, detecting trends in 100-year extremes as well as analysing drivers of extreme events that are difficult to discern from limited observational records.
How to cite: Kelder, T., Slater, L., Marjoribanks, T., Wilby, R., Prudhomme, C., and Wagemann, J.: Seasonal predictions as a high-resolution large ensemble to study extreme events over recent decades, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12136, https://doi.org/10.5194/egusphere-egu21-12136, 2021.
The UNSEEN (UNprecedented Simulated Extremes using ENsembles) method involves using a large ensemble of initialised climate model simulations to increase the sample size of rare events. In this work we extended UNSEEN to focus on intense summertime daily rainfall events. Specifically, plausible extreme rainfall scenarios were developed to help understand potential surface water flooding impacts, and ultimately better inform flood management and resilience across the UK. To help address modelling limitations a large ensemble of simulations from two climate models were used; an initialised 25km global model that uses parametrized convection, and a dynamically downscaled 2.2km model that uses explicit convection. Climate model fidelity was assessed using a regional pooling technique based on extreme value theory. Across much of the UK both models are indistinguishable from the observations in terms of the statistical characteristics which govern the magnitude of very rare return periods. The UNSEEN analysis provides new estimates of plausible extreme return levels (i.e. 1-in-1000 year) across the UK and can reduce uncertainty in the expected frequency of very rare events by 50-70% compared to estimates using observations alone. These results enable suitable observed rainfall profiles to be uplifted to plausible extreme return levels, which can then be used within regional hydrological models to stress test surface flooding scenarios. The annual chance of unprecedented daily rainfall events in the current climate is also quantified, and found to be up to 5% (1-in-20 year return level) for many grid cells across southern parts of the UK. Finally, a significant benefit of the UNSEEN approach over purely statistical emulators is the use of dynamical climate models which allow the large-scale dynamical drivers of extreme daily summertime rainfall to be assessed.
How to cite: Kent, C., Dunstone, N., Tucker, S., Scaife, A., Kendon, E., Brown, S., Smith, D., Greenwood, S., and McLean, L.: Unprecedented summertime daily rainfall across the UK, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7258, https://doi.org/10.5194/egusphere-egu21-7258, 2021.
To estimate the impact of climate change on our society we need to use climate projections based on numerical models. These models make it possible to assess the effects on climate of the increase in greenhouse gases (GHG) as well as natural variability. We know that the global average temperature will increase and that the occurrence, intensity and spatio-temporal distribution of extreme precipitations will change. These extreme weather events cause droughts, floods and other natural disasters that have significant consequences on our life and environment. Precipitation is a key variable in adapting to climate change.
This study focuses on the ClimEx large ensemble, a set of 50 independent simulations created to study the effect of climate change and natural variability on the water network in Quebec. This dataset consists of simulations produced using the Canadian Regional Climate Model version 5 (CRCM5) at 12 km of resolution driven by simulations from the second generation Canadian Earth System Model (CanESM2) global model at 310 km of resolution.
The aim of the project is to evaluate the performance of the ClimEx ensemble in simulating the daily cycle and representing extreme values. To get there, 30 years of hourly time series for precipitation and 3 hourly for temperature are analyzed. The simulations are compared with the values from the simulation of CRCM5 driven by ERA-Interim reanalysis, the ERA5 reanalysis and Environment and Climate Change Canada (ECCC) stations. An evaluation of the sensitivity of different statistics to the number of members is also performed.
The daily cycle of precipitation from ClimEx shows mainly non-significant correlations with the other datasets and its amplitude is less than the observation datas from ECCC stations. For temperature, the correlation is strong and the amplitude of the cycle is similar to observations. ClimEx provides a fairly good representation of the 95, 97, 99th quantiles for precipitation. For temperature it represents a good distribution of quantiles but with a warm bias in southern Quebec. For precipitation hourly maximum, ClimEx shows values 10 times higher than ERA5. For temperature, minimum and maximum values may exceed the ERA5 limit by up to 20°C. For precipitation, the minimum number of members for the estimation of the 95 and 99thquantiles and the mean cycle is between 15 and 50 for an estimation error of less than 5%. For the 95, 99th quantiles of temperature, the minimum number of members is between 1 and 17 and for the mean cycle 1 to 2 members are necessary to obtain an estimation error of less than 0.5°C.
How to cite: Begin, A.-M.: Evaluation of the daily cycle in the simulation of the ClimEx large-ensemble, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12097, https://doi.org/10.5194/egusphere-egu21-12097, 2021.
We analyze several sets of global and regional climate models (GCMs and RCMs) to investigate how robust climate change signals for seasonal mean and extreme precipitation are. The projections of the regional climate models ENSEMBLES and EURO-CORDEX are used along with projections of their driving global data sets of CMIP3 and CMIP5, respectively. In addition, projections of CMIP6 and the high-resolution HighResMIP global models are used. The projections are used with high emission scenarios (A1B or RCP8.5) depending on availability. To calculate the climate change signals a future period 2071-2100 and a baseline period 1971-2000 is chosen. For comparability and to reduce the uncertainty by the choice of the emission scenario, the climate change signals are normalized by the European mean surface temperature. We make statements of percentage change per degree warming. The analyses are carried out for eight European sub-regions: Alps, British Isles, Iberian Peninsula, France, Mid-Europe, Scandinavia, Mediterranean and Eastern Europe. We define extreme precipitation as the 20-year return values of each season. Regarding mean precipitation the climate change signals are robust across the different data sets. In accordance with previous studies, there is a transition zone between increasing and decreasing signals which is located in southern Europe in winter and more north in summer. This seasonal cycle can be found for all regions. For extreme precipitation, the climate change signals indicating increases in all seasons and regions. Especially in summer, in most regions the RCMs showing a higher increase compared to the GCMs up to a difference of about 5%/K for the ensemble medians. Hence, the signals for extremes are not that robust than for means.
To understand where these differences come from, we are using a precipitation scaling for extremes to investigate the thermodynamic and dynamic contributions. The thermodynamic contribution shows homogeneous increasing signals for Europe. This means the dynamic contribution is the key to understand differences between the model ensembles.
We aim to understand the discrepancy between different lines of evidence and focusing our study in the field of climate information distillation.
How to cite: Ritzhaupt, N. and Maraun, D.: Robustness of projections of European precipitation for seasonal means and seasonal extremes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15105, https://doi.org/10.5194/egusphere-egu21-15105, 2021.
Climate models are primary tools to reconstruct past and predict future climates. It is common procedure to use general circulation models (GCMs) for large scale studies and regional climate models (RCMs), for impact studies at a finer spatial resolution. However, climate models face biases compared to observation. To overcome these biases, different statistical methods have been suggested in the scientific literature that employ a transformation algorithm to re-scale (or bias-correct) RCM outputs. Some of these methods (e.g. univariate methods that adjust only one RCM-simulated variable at a time) are comparatively easy to implement while others (e.g., multi-variate correction that guarantees consistency in spatiotemporal fields and different climate variables) that have been introduced lately to the field, are more complex and require advanced statistical knowledge and more computing power. Therefore, the need to further investigate the performance of the latest more complex bias-adjustment methods under different climatic conditions still exists and their added value still needs to be evaluated from different aspects.
Thus, we assessed the skill of two commonly used multivariate methods, namely copula based bias adjustment methods and non-parametric n-dimensional multivariate bias correction (MBCn). We further compared them with widely used univariate methods, i.e. the parametric distribution mapping (DS) and the non-parametric quantile delta mapping (QDM), to adjust RCM-simulated temperature and precipitation. We evaluated these methods over 55 Swedish catchments varying in size and climatic features using an ensemble of 10 different RCMs under varying climate conditions to check multiple features that represent both probabilistic and temporal behavior. To evaluate how these methods, perform in nonstationary climate conditions, we performed the assessment over two periods of 22 years each, where the period 1961-1982 is used for calibration and 1983-2004 for validation. The adequacy of each bias adjustment method in reducing the biases varies depending on several factors such as the studied watershed, the applied RCM model, utilized climate variable and the statistical feature that is subjected to adjustment. We further discuss potential issues and trade-offs of each of the applied methods and present an evaluation of each bias-corrected climate variable in terms of its (1) statistical properties, (2) temporal behavior utilizing cross correlation and autocorrelation measures, and (3) dependence structure to the other variable with help of copula-based dependence measures. Finally, we also examined how the four bias-adjustment methods influence the Clausius Clapeyron relation, which serves as an important climatic illustration of the relationship between extreme precipitation and temperature.
How to cite: Tootoonchi, F., Haerter, J. O., Raty, O., Grabs, T., and Teutschbein, C.: Advances and challenges in the past decade: from univariate to multivariate bias adjustment of climate models for impact studies, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13062, https://doi.org/10.5194/egusphere-egu21-13062, 2021.
Robust information of hydrometeorological extremes is important for effective risk management, mitigation and adaptation measures by public authorities, civil and engineers dealing for example with water management. Typically, return values of certain variables, such as extreme precipitation and river discharge, are of particular interest and are modelled statistically using Extreme Value Theory (EVT). However, the estimation of these rare events based on extreme value analysis are affected by short observational data records leading to large uncertainties.
In order to overcome this limitation, we propose to use the latest seasonal meteorological prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF SEAS5) and seasonal hydrological forecasts generated with the pan-European E-HYPE model of the original period 1993-2015 and to extend the dataset to longer synthetic time series by pooling single forecast months to surrogate years. To ensure an independent dataset, the seasonal forecast skill is assessed in advance and months (and lead months) with positive skill are excluded. In this study, we simplify the method and work with samples of 6- and 4-month forecasts (instead of the full 7-month forecasts) depending on the statistical independency of the variables. It enables the record to be extended from the original 23 years to 3450 and 2300 surrogate years for the 6- and 4-month forecasts respectively.
Furthermore, we investigate the robustness of estimated 50- and 100-year return values for extreme precipitation and river discharge using 1-year block maxima that are fitted to the Generalized Extreme Value distribution. Surrogate sets of pooled years are randomly constructed using the Monte-Carlo approach and different sample sizes are chosen. This analysis reveals a considerable reduction in the uncertainty of all return period estimations for both variables for selected locations across Europe using a sample size of 500 years. This highlights the potential in using the ensembles of meteorological and hydrological seasonal forecasts to obtain timeseries of sufficient length and minimize the uncertainty in the extreme value analysis.
How to cite: Klehmet, K., Berg, P., Bozhinova, D., Crochemore, L., Pechlivanidis, I., Photiadou, C., and Yang, W.: Robustness of precipitation and river discharge extremes in the surrogate world of seasonal forecasts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7126, https://doi.org/10.5194/egusphere-egu21-7126, 2021.
Recent studies show that the frequency and intensity of extreme precipitation will increase under a warmer climate. It is expected that extreme convective precipitation will scale at a larger than Clausius–Clapeyron rate and especially so for short-duration rainfall. This has implication on flooding risk, and especially so on small catchments (<500 km2) which have a quick response time and are therefore particularly vulnerable to short duration rainfall. The impact of the amplification of extreme precipitation as a function of catchment scale has not been widely studied because most of the climate change impact studies have been conducted at the daily time step or higher. This is because until recently the vast majority of climate model outputs have only been available at the daily time step.
This study has looked at the amplification of sub-daily, daily, and multiday extreme precipitation and flooding and its dependency on catchment scale. This work uses outputs from the Climex large-ensemble to study the amplification of extreme streamflow with return period from 2 to 300 years and durations from 1 to 24 hours over 133 North-American catchments. Using a large ensemble allows for the accurate empirical computation of extreme events with very large return periods. Results indicate that future extreme streamflow relative increases are largest for smaller catchments, longer return period, and shorter rainfall durations. Small catchments are therefore more vulnerable to future extreme rainfall than their larger counterparts.
How to cite: Faghih, M., Brissette, F., Sabeti, P., and Tarek, M.: Using a high-resolution regional climate model large ensemble to simulate the impact of extreme precipitation on flooding over small to medium-size catchments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10249, https://doi.org/10.5194/egusphere-egu21-10249, 2021.
Hydropower is a renewable source of energy that relies on efficient water planning and management. As the behavior of this natural resource is difficult to predict, water managers therefore use methods to help the decision-making process. Reinforcement Learning (RL) has been shown to be a potentially effective approach to overcome the limitations of the Stochastic Dynamic Programming (SDP) method that is commonly used for water management. However, convergence to a robust and efficient operating policy from RL methods requires large amounts of data, while long-term historical data is not always available. The objective of this study consists in using tools to generate long-term hydrological series to obtain an efficient parameterization of the management policy. This presentation introduces a comparison of calibration datasets used in a RL method for the optimal control of a hydropower system. This method aims to find a feedback policy that maximizes the production of a hydropower system over a mid-term horizon. Three streamflow datasets are compared on a real hydropower system for RL calibration: 1) the historical streamflow (35 years), 2) streamflow simulated by a hydrological model driven by a high-resolution large-ensemble climate model data (3500 years) from the ClimEx project, and 3) streamflow simulated by a hydrological model driven by climate data generated with a stochastic weather generator (5000 years). The GR4J hydrological model is employed for the hydrologic modelling aspect of the work. The reinforcement learning method is applied on the Lac-Saint-Jean water resources system in Quebec (Canada), where the hydrological regime is snowmelt-dominated. A bootstrapping method where multiple calibration and validation sets were resampled is used to conduct a robust statistical analysis for comparing the methods’ performance. The performance of the calibrated management policy is evaluated with respect to the operational constraints of the system as well as the overall energy production. Preliminary results show that is possible to achieve effective management policies by using tools to generate long-term hydrological series to feed a RL method.
How to cite: Dallaire, G., Arsenault, R., Côté, P., and Demeester, K.: Calibration of a reinforcement learning method with the ClimEx large ensemble and a weather generator for water management , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12931, https://doi.org/10.5194/egusphere-egu21-12931, 2021.
Understanding the effect of climate change on global economic growth is critical to informing optimal mitigation and adaptation policy. Many recent efforts have been made to empirically quantify the roles of weather and climate in economic growth, but these efforts have generally focused on changes in mean climate rather than changes in climate variability. Climate change is expected to alter modes of climate variability, so fully quantifying the costs of climate change requires both understanding the effects of climate variability on economic growth and constraining how this variability will evolve under forcing. Here we combine historical climate and economic data with multiple climate model ensembles to quantify the economic growth effects of El Niño and examine how these effects evolve in the 21st century. Preliminary results show substantial negative effects of El Niño on growth, with historical events reducing growth by >5 percentage points over 5 years in countries whose temperature variability is tightly correlated with ENSO. We then examine how climate change influences El Niño and its growth effects in both multi-model and single-model ensembles, allowing us to isolate the role of internal climate variability in shaping the evolution of ENSO statistics in the 21st century. Climate change is generally projected to increase El Niño frequency and thus the resulting growth penalties, but internal variability generates a wide spread of responses, all of which are consistent with the same forcing. These results highlight how internal variability can influence both interannual El Niño occurrence and long-term changes in its statistics, with consequences for future economic growth. Moreover, these results illustrate the range of climate impact trajectories that are consistent with the same emissions, providing critical information for adaptation decision-makers needing to construct robust socioeconomic systems in the face of 21st century climate change.
How to cite: Callahan, C. and Mankin, J.: El Niño variability mediates 21st century growth effects of climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8023, https://doi.org/10.5194/egusphere-egu21-8023, 2021.
Human influence on climate is not usually disentangled in the contribution of single emitters, especially when assessing changes and impacts in individual countries. However, such information could help individual countries understand their role in driving climate change and thus aid them in committing to fair and evidence-based emission reduction targets. Here, we quantify the contribution of single emitters to country-level median warming and extremes based on historical emissions and currently pledged policy targets. Thereby, we focus on the five largest historical emitters – China, the United States of America, the European Union, India, and Russia. While large ensembles are needed for this task, the computational burden of running full Earth System Models (ESMs) renders it impossible to answer our question with actual ESMs. Instead, we combine a physical global mean temperature emulator (Meinshausen et al., 2009) with a statistical spatially-resolved ESM emulator (Beusch et al., 2020) to create millions of temperature field time series. Our setup accounts for three major sources of uncertainty: (i) uncertainty in the global temperature response to greenhouse gas emissions, (ii) uncertainty in the regional response to global warming, (iii) uncertainty due to internal climate variability.
We find that historically rare hot years (occurring about once every 100 years in pre-industrial times) are expected at least every second year in 89 % (likely range: 71 – 100 %) of all countries by 2030. Without the emissions of the top five emitters over the time period during which policy makers had been informed about the looming anthropogenic climate crisis, i.e., after the first IPCC report of 1990, it would be 40 % (10 – 64 %) of all countries instead. Furthermore, when considering all current and projected emissions until 2030, 8 % (0 – 54 %) of countries are headed towards surpassing 2.0 °C of warming since pre-industrial times by 2030. If all nations followed the same per capita emissions as the USA since the 2015 Paris Agreement, the percentage of countries surpassing 2.0 °C by 2030 would amount to 78 % (24 – 96 %). Generally, northern high latitude countries experience the largest changes in median warming and tropical Africa the largest changes in extremes. Our results emphasize the relevance of individual emitters, and in particular the top five emitters, in driving regional climate change across different time periods.
Beusch, L., Gudmundsson, L., and Seneviratne, S. I. (ESD, 2020): https://doi.org/10.5194/esd-11-139-2020
Meinshausen, M., Meinshausen, N., Hare, W. et al. (Nature, 2009): https://doi.org/10.1038/nature08017
How to cite: Beusch, L., Nauels, A., Gudmundsson, L., Schleussner, C.-F., and Seneviratne, S. I.: Assigning responsibility for country-level warming to individual major emitters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8614, https://doi.org/10.5194/egusphere-egu21-8614, 2021.
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