Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe

The final publication is available at Springer via http://dx.doi.org/10.1007/s00382-015-2879-4Land surfaces and soil conditions are key sources of climate predictability at the seasonal time scale. In order to estimate how the initialization of the land surface affects the predictability at seasonal time scale, we run two sets of seasonal hindcasts with the general circulation model EC-Earth2.3. The initialization of those hindcasts is done either with climatological or realistic land initialization in May using the ERA-Land re-analysis. Results show significant improvements in the initialized run occurring up to the last forecast month. The prediction of near-surface summer temperatures and precipitation at the global scale and over Europe are improved, as well as the warm extremes prediction. As an illustration, we show that the 2010 Russian heat wave is only predicted when soil moisture is initialized. No significant improvement is found for the retrospective prediction of the 2003 European heat wave, suggesting this event to be mainly large-scale driven. Thus, we confirm that late-spring soil moisture conditions can be decisive in triggering high-impact events in the following summer in Europe. Accordingly, accurate land-surface initial conditions are essential for seasonal predictions.The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under grant agreements 308378 (SPECS), 282378 (DEN-FREE) and 607085 (EUCLEIA), and from the Spanish Ministerio de Economía y Competitividad (MINECO) under the project CGL2013-41055-R. We acknowledge the s2dverification R-based package (http://cran.r-project.org/web/packages/s2dverification/index.html). We also thank ECMWF for providing the ERA-Land initial conditions and computing resources through the SPICCF Special Project.Peer ReviewedPostprint (author's final draft

Detecting improvements in forecast correlation skill: Statistical testing and power analysis

This is the final version. Available from the American Meteorological Society via the DOI in this recordThe skill of weather and climate forecast systems is often assessed by calculating the correlation coefficient between past forecasts and their verifying observations. Improvements in forecast skill can thus be quantified by correlation differences. The uncertainty in the correlation difference needs to be assessed to judge whether the observed difference constitutes a genuine improvement, or is compatible with random sampling variations. A widely used statistical test for correlation difference is known to be unsuitable, because it assumes that the competing forecasting systems are independent. In this paper, appropriate statistical methods are reviewed to assess correlation differences when the competing forecasting systems are strongly correlated with one another. The methods are used to compare correlation skill between seasonal temperature forecasts that differ in initialization scheme and model resolution. A simple power analysis framework is proposed to estimate the probability of correctly detecting skill improvements, and to determine the minimum number of samples required to reliably detect improvements. The proposed statistical test has a higher power of detecting improvements than the traditional test. The main examples suggest that sample sizes of climate hindcasts should be increased to about 40 years to ensure sufficiently high power. It is found that seasonal temperature forecasts are significantly improved by using realistic land surface initial conditions.The authors acknowledge support by the European Union Program FP7/2007-13 under Grant Agreement 3038378 (SPECS). The work of O. Bellprat was funded by ESA under the Climate Change Initiative (CCI) Living Planet Fellowship VERITAS-CCI

Sensitivity of winter North Atlantic-European climate to resolved atmosphere and ocean dynamics

Northern Hemisphere western boundary currents, like the Gulf Stream, are key regions for cyclogenesis affecting large-scale atmospheric circulation. Recent observations and model simulations with high-temporal and -spatial resolution have provided evidence that the associated ocean fronts locally affect troposphere dynamics. A coherent view of how this affects the mean climate and its variability is, however, lacking. In particular the separate role of resolved ocean and atmosphere dynamics in shaping the atmospheric circulation is still largely unknown. Here we demonstrate for the first time, by using coupled seasonal forecast experiments at different resolutions, that resolving meso-scale oceanic variability in the Gulf Stream region strongly affects mid-latitude interannual atmospheric variability, including the North Atlantic Oscillation. Its impact on climatology, however, is minor. Increasing atmosphere resolution to meso-scale, on the other hand, strongly affects mean climate but moderately its variability. We also find that regional predictability relies on adequately resolving small-scale atmospheric processes, while resolving small-scale oceanic processes acts as an unpredictable source of noise, except for the North Atlantic storm-track where the forcing of the atmosphere translates into skillful predictions

Earth System Model Evaluation Tool (ESMValTool) v2.0 – diagnostics for emergent constraints and future projections from Earth system models in CMIP

The Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for evaluation and analysis of Earth system models (ESMs), is designed to facilitate a more comprehensive and rapid comparison of single or multiple models participating in the Coupled Model Intercomparison Project (CMIP). The ESM results can be compared against observations or reanalysis data as well as against other models including predecessor versions of the same model. The updated and extended version (v2.0) of the ESMValTool includes several new analysis scripts such as large-scale diagnostics for evaluation of ESMs as well as diagnostics for extreme events, regional model and impact evaluation. In this paper, the newly implemented climate metrics such as effective climate sensitivity (ECS) and transient climate response (TCR) as well as emergent constraints for various climate-relevant feedbacks and diagnostics for future projections from ESMs are described and illustrated with examples using results from the well-established model ensemble CMIP5. The emergent constraints implemented include constraints on ECS, snow-albedo effect, climate–carbon cycle feedback, hydrologic cycle intensification, future Indian summer monsoon precipitation and year of disappearance of summer Arctic sea ice. The diagnostics included in ESMValTool v2.0 to analyze future climate projections from ESMs further include analysis scripts to reproduce selected figures of chapter 12 of the Intergovernmental Panel on Climate Change's (IPCC) Fifth Assessment Report (AR5) and various multi-model statistics.This research has been supported by the Horizon 2020 Framework Programme (CRESCENDO (grant no. 641816), 4C (grant no. 821003), and IS-ENES3 (grant no. 824084)), the Copernicus Climate Change Service (C3S) (Metrics and Access to Global Indices for Climate Change Projections (MAGIC)), the Federal Ministry of Education and Research (BMBF) (CMIP6-DICAD), the European Space Agency (ESA Climate Change Initiative Climate Model User Group (ESA CCI CMUG)) and the Helmholtz Association (Advanced Earth System Model Evaluation for CMIP (EVal4CMIP)).Peer Reviewed"Article signat per 13 autors/es: Axel Lauer, Veronika Eyring, Omar Bellprat, Lisa Bock, Bettina K. Gier, Alasdair Hunter, Ruth Lorenz, Núria Pérez-Zanón, Mattia Righi, Manuel Schlund, Daniel Senftleben, Katja Weigel, and Sabrina Zechlau"Postprint (published version

Extreme precipitation in the Netherlands: An event attribution case study

Attributing the change in likelihood of extreme weather events, particularly those occurring at small spatiotemporal scales, to anthropogenic forcing is a key challenge in climate science. While a warmer world is associated with an increase in atmospheric moisture on a global scale, the impact on the magnitude of extreme precipitation episodes has substantial regional variability. Analysis of individual cases is important in understanding the extent of these changes on spatial scales relevant to stakeholders. Here, we present a probabilistic attribution analysis of the extreme precipitation that fell in large parts of the Netherlands on 28 July 2014. Using a step-by-step approach, we aim to identify changes in intensity and likelihood of such an event as a result of anthropogenic global warming while highlighting the challenges in performing robust event attribution on high-impact precipitation events that occur at small scales. A method based on extreme value theory is applied to observational data in addition to global and regional climate model ensembles that pass a robust model evaluation process. Results based on observations suggest a strong and significant increase in the intensity and frequency of a 2014-type event as a result of anthropogenic climate change but trends in the model ensembles used are considerably smaller. Our results are communicated alongside considerable uncertainty, highlighting the difficulty in attributing events of this nature. Application of our approach to convection-resolving models may produce a more robust attribution.</p

Unusual past dry and wet rainy seasons over Southern Africa and South America from a climate perspective

Southern Africa and Southern South America have experienced recent extremes in dry and wet rainy seasons which have caused severe socio-economic damages. Selected past extreme events are here studied, to estimate how human activity has changed the risk of the occurrence of such events, by applying an event attribution approach (Stott et al., 2004)comprising global climate models of Coupled Model Intercomparison Project 5 (CMIP5). Our assessment shows that models' representation of mean precipitation variability over Southern South America is not adequate to make a robust attribution statement about seasonal rainfall extremes in this region. Over Southern Africa, we show that unusually dry austral summers as occurred during 2002/2003 have become more likely, whereas unusually wet austral summers like that of 1999/2000 have become less likely due to anthropogenic climate change. There is some tentative evidence that the risk of extreme high 5-day precipitation totals (as observed in 1999/2000) have increased in the region. These results are consistent with CMIP5 models projecting a general drying trend over SAF during December–January–February (DJF) but also an increase in atmospheric moisture availability to feed heavy rainfall events when they do occur. Bootstrapping the confidence intervals of the fraction of attributable risk has demonstrated estimates of attributable risk are very uncertain, if the events are very rare. The study highlights some of the challenges in making an event attribution study for precipitation using seasonal precipitation and extreme 5-day precipitation totals and considering natural drivers such as ENSO in coupled ocean–atmosphere models

Earth System Model Evaluation Tool (ESMValTool) v2.0 - diagnostics for emergent constraints and future projections from Earth system models in CMIP

The Earth System Model Evaluation Tool (ESMValTool), a community diagnostics and performance metrics tool for evaluation and analysis of Earth system models (ESMs) is designed to facilitate a more comprehensive and rapid comparison of single or multiple models participating in the Coupled Model Intercomparison Project (CMIP). The ESM results can be compared against observations or reanalysis data as well as against other models including predecessor versions of the same model. The updated and extended version 2.0 of the ESMValTool includes several new analysis scripts such as large-scale diagnostics for evaluation of ESMs as well as diagnostics for extreme events, regional model and impact evaluation. In this paper, the newly implemented climate metrics such as effective climate sensitivity (ECS) and transient climate response (TCR) as well as emergent constraints for various climate-relevant feedbacks and diagnostics for future projections from ESMs are described and illustrated with examples using results from the well-established model ensemble CMIP5. The emergent constraints implemented include constraints on ECS, snow-albedo effect, climate-carbon cycle feedback, hydrologic cycle intensification, future Indian summer monsoon precipitation, and year of disappearance of summer Arctic sea ice. The diagnostics included in ESMValTool v2.0 to analyze future climate projections from ESMs further include analysis scripts to reproduce selected figures of chapter 12 of the Intergovernmental Panel on Climate Change's (IPCC) Fifth Assessment report (AR5) and various multi-model statistics

Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate

Climate models serve as indispensable tools to investigate the effect of anthropogenic emissions on current and future climate, including extremes. However, as low-dimensional approximations of the climate system, they will always exhibit biases. Several attempts have been made to correct for biases as they affect extremes prediction, predominantly focused on correcting model-simulated distribution shapes. In this study, the effectiveness of a recently published quantile-based bias correction scheme, as well as a new subset selection method introduced here, are tested out-of-sample using model-as-truth experiments. Results show that biases in the shape of distributions tend to persist through time, and therefore, correcting for shape bias is useful for past and future statements characterizing the probability of extremes. However, for statements characterized by a ratio of the probabilities of extremes between two periods, we find that correcting for shape bias often provides no skill improvement due to the dominating effect of bias in the long-term trend. Using a toy model experiment, we examine the relative importance of the shape of the distribution versus its position in response to long-term changes in radiative forcing. It confirms that the relative position of the two distributions, based on the trend, is at least as important as the shape. We encourage the community to consider all model biases relevant to their metric of interest when using a bias correction procedure and to construct out-of-sample tests that mirror the intended application

Using EC-Earth for climate prediction research

Climate prediction at the subseasonal to interannual time range is now performed routinely and operationally by an increasing number of institutions. The feasibility of climate prediction largely depends on the existence of slow and predictable variations in the ocean surface temperature, sea ice, soil moisture and snow cover, and on our ability to model the atmosphere’s interactions with those variables. Climate prediction is typically performed with statistical-empirical or process-based models. The two methods are complementary. Although forecasting systems using global climate models (GCMs) have made substantial progress in the last few decades (Doblas-Reyes et al., 2013), systematic errors and misrepresentations of key processes still limit the value of dynamical prediction in certain areas of the globe. At the same time, model initialisation, ensemble generation, understanding the processes at the origin of predictability, forecasting extremes, bias adjustment and model evaluation are all challenging aspects of the climate prediction problem. Addressing them requires both a large base of researchers with expertise in physics, mathematics, statistics, high-performance computing and data analysis interested in climate prediction issues and a tool for them to work with. This article illustrates how one of these tools, the EC-Earth climate model (Box A), has been used to train scientists in climate prediction and to address scientific challenges in this field. The use of model components from ECMWF’s Integrated Forecasting System (IFS) in EC-Earth means that some of the results obtained with EC-Earth can feed back into ECMWF’s activities. EC-Earth has been run extensively on ECMWF’s high-performance computing facility (HPCF), among a range of HPCFs across Europe and North America. The availability of ECMWF’s HPCF to EC-Earth partners, including the use of the successful ECMWF Special Project programme, means that a substantial amount of EC-Earth’s collaborative work, both within the consortium and with ECMWF, takes place on this platform.Postprint (published version

The role of anomalous SST and surface fluxes over the Southeastern North Atlantic in the explosive development of windstorm Xynthia

In late February 2010 the extraordinary windstorm Xynthia crossed over Southwestern and Central Europe and caused severe damage, affecting particularly the Spanish and French Atlantic coasts. The storm was embedded in uncommon large-scale atmospheric and boundary conditions prior to and during its development, namely enhanced sea surface temperatures (SST) within the low-level entrainment zone of air masses, an unusual southerly position of the polar jet stream, and a remarkable split jet structure in the upper troposphere. To analyse the processes that led to the rapid intensification of this exceptional storm originating close to the subtropics (30°N), the sensitivity of the cyclone intensification to latent heat release is determined using the regional climate model COSMO-CLM forced with ERA-Interim data. A control simulation with observed SST shows that moist and warm air masses originating from the subtropical North Atlantic were involved in the cyclogenesis process and led to the formation of a vertical tower with high values of potential vorticity (PV). Sensitivity studies with reduced SST or increased laminar boundary roughness for heat led to reduced surface latent heat fluxes. This induced both a weaker and partly retarded development of the cyclone and a weakening of the PV-tower together with reduced diabatic heating rates, particularly at lower and mid levels. We infer that diabatic processes played a crucial role during the phase of rapid deepening of Xynthia and thus to its intensity over the Southeastern North Atlantic. We suggest that windstorms like Xynthia may occur more frequently under future climate conditions due to the warming SSTs and potentially enhanced latent heat release, thus increasing the windstorm risk for Southwestern Europe