Observed modes of sea surface temperature variability in the South Pacific region

The South Pacific (SP) region exerts large control on the climate of the Southern Hemisphere at many times scales. This paper identifies the main modes of interannual sea surface temperature (SST) variability in the SP which consist of a tropical-driven mode related to a horseshoe structure of positive/negative SST anomalies within midlatitudes and highly correlated to ENSO and Interdecadal Pacific Oscillation (IPO) variability, and another mode mostly confined to extratropical latitudes which is characterized by zonal propagation of SST anomalies within the South Pacific Gyre. Both modes are associated with temperature and rainfall anomalies over the continental regions of the Southern Hemisphere. Besides the leading mode which is related to well known warmer/cooler and drier/moister conditions due to its relationship with ENSO and the IPO, an inspection of the extratropical mode indicates that it is associated with distinct patterns of sea level pressure and surface temperature advection. These relationships are used here as plausible and partial explanations to the observed warming trend observed within the Southern Hemisphere during the last decades.The authors would like to thank Scott Power for his comments on an earlier version of the manuscript and the two anonymous reviewers whose suggestions led to a substantial improvement of the paper. This study was supported by Grants UBACyT-20020100100803, UBACyT-20020120300051, PIP-11220120100586 and the SPECS (GA 308378) EU-funded Project. JG-S was partially supported by the H2020-funded MSCA-IF-EF DPETNA project (GA No. 655339). The authors acknowledge the Red Española de Supercomputación (RES) and PRACE for awarding access to MareNostrum 3 at the Barcelona Supercomputing Center through the HiResClim project. The support of Virginie Guémas and Oriol Mula-Valls at the Barcelona Supercomputing Center is warmly appreciated.Peer ReviewedPostprint (author's final draft

Linking the anomaly initialization approach to the mapping paradigm: a proof-of-concept study

Seasonal-to-decadal predictions are initialized using observations of the present climatic state in full field initialization (FFI). Such model integrations undergo a drift toward the model attractor due to model deficiencies that incur a bias in the model. The anomaly initialization (AI) approach reduces the drift by adding an estimate of the bias onto the observations at the expense of a larger initial error. In this study FFI is associated with the fidelity paradigm, and AI is associated with an instance of the mapping paradigm, in which the initial conditions are mapped onto the imperfect model attractor by adding a fixed error term; the mapped state on the model attractor should correspond to the nature state. Two diagnosis tools assess how well AI conforms to its own paradigm under various circumstances of model error: the degree of approximation of the model attractor is measured by calculating the overlap of the AI initial conditions PDF with the model PDF; and the sensitivity to random error in the initial conditions reveals how well the selected initial conditions on the model attractor correspond to the nature states. As a useful reference, the initial conditions of FFI are subjected to the same analysis. Conducting hindcast experiments using a hierarchy of low-order coupled climate models, it is shown that the initial conditions generated using AI approximate the model attractor only under certain conditions: differences in higher-than-first-order moments between the model and nature PDFs must be negligible. Where such conditions fail, FFI is likely to perform better

Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state

Decadal prediction exploits sources of predictability from both the internal variability through the initialisation of the climate model from observational estimates, and the external radiative forcings. When a model is initialised with the observed state at the initial time step (Full Field Initialisation—FFI), the forecast run drifts towards the biased model climate. Distinguishing between the climate signal to be predicted and the model drift is a challenging task, because the application of a-posteriori bias correction has the risk of removing part of the variability signal. The anomaly initialisation (AI) technique aims at addressing the drift issue by answering the following question: if the model is allowed to start close to its own attractor (i.e. its biased world), but the phase of the simulated variability is constrained toward the contemporaneous observed one at the initialisation time, does the prediction skill improve? The relative merits of the FFI and AI techniques applied respectively to the ocean component and the ocean and sea ice components simultaneously in the EC-Earth global coupled model are assessed. For both strategies the initialised hindcasts show better skill than historical simulations for the ocean heat content and AMOC along the first two forecast years, for sea ice and PDO along the first forecast year, while for AMO the improvements are statistically significant for the first two forecast years. The AI in the ocean and sea ice components significantly improves the skill of the Arctic sea surface temperature over the FFI.The authors acknowledge funding support for this study from the SPECS (ENV-2012-308378) project funded by the Seventh Framework Programme (FP7) of the European Commission and the PICA-ICE (CGL2012-31987) project funded by the Ministry of Economy and Competitiveness of Spain. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center.Peer ReviewedPostprint (author's final draft

Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison

Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980–2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.The authors acknowledge funding support from the COPERNICUS action CLIM4ENERGY-Climate for Energy (C3S 441 Lot 2), the New European Wind Atlas (NEWA) project funded by ERA-NET Plus, Topic FP7 ENERGY.2013.10.1.2, the RESILIENCE (CGL2013–41055-R) project, funded by the Spanish Ministerio de Economía y Competitividad (MINECO), and the FP7 EUPORIAS (GA 308291) and SPECS (GA 308378) projects. Thanks to Daniel Cabezón and Sergio Lozano for their valuable feedback. We acknowledge the s2dverification R-based package (http://cran.rproject. org/web/packages/s2dverification) developers. Finally, we would like to thank Pierre-Antoine Bretonniere, Júlia Giner, Nicolau Manubens and Javier Vegas for their technical support at different stages of this project.Peer ReviewedPostprint (published version

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

The match between climate services demands and Earth System Models supplies

Earth System Models (ESM) are key ingredients of many of the climate services that are currently being developed and delivered. However, ESMs have more applications than the provision of climate services, and similarly many climate services use more sources of information than ESMs. This discussion paper elaborates on dilemmas that are evident at the interface between ESMs and climate services, in particular: (a) purposes of the models versus service development, (b) gap between the spatial and temporal scales of the models versus the scales needed in applications, and (c) Tailoring climate model results to real-world applications. A continued and broad-minded dialogue between the ESM developers and climate services providers’ communities is needed to improve both the optimal use and direction of ESM development and climate service development. We put forward considerations to improve this dialogue between the communities developing ESMs and climate services, in order to increase the mutual benefit that enhanced understanding of prospects and limitations of ESMs and climate services will bring.This work and its contributors (B. van den Hurk, C. Hewitt, J. Bessembinder, F. Doblas-Reyes, R. Döscher) were funded by the Horizon 2020 Framework Programme of the European Union: Project ref. 689029 (Climateurope project). The co-author and editor of the journal states that she was not involved in the review process of the paper.Peer ReviewedPostprint (published version

State-of-the-art climate predictions for energy climate services

Seasonal predictions of 10-m wind speed can be used by the wind energy sector in a number of decision making processes. Two different techniques of post-processing are applied in order to correct the unavoidable systematic errors present in all forecast systems. Besides an assessment of the impact of these corrections on the quality of the probabilistic forecast system is provided

Dynamical prediction of Arctic sea ice modes of variability

This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.The authors acknowledge funding support for this study from the PICA-ICE (CGL2012-31987) Project funded by the Ministry of Economy and Competitiveness of Spain, the SPECS (GA 308378) Project funded by the Seventh Framework Programme (FP7) and the PRIMAVERA (GA 641727) project funded by the Horizon 2020 framework of the European Commission. NSF was a recipient of the Juan de la Cierva-incorporación postdoctoral fellowship from the Ministry of Economy and Competitiveness of Spain. NCJ was supported by NOAA’s Climate Program Office. The authors acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center in Barcelona, Spain, and by the European Centre for Medium–Range Weather Forecasts in Reading, UK. The authors thank Stefan Siegert and an anonymous reviewer for their constructive inputs, and Francois Massonnet, Javier Garcia-Serrano, Omar Bellprat, Louis-Philippe Caron, Matthieu Chevallier, Torben Koening, Mitch Bushuk and Jonathan Day for valuable discussions. Analyzed global sea ice historical reconstruction with ORCA1 NEMO-LIM2 is available upon request.Peer ReviewedPostprint (published version