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A. Bellucci, A. Mariotti, and S. Gualdi

Abstract

Results from a study inspecting the origins of multidecadal variability in the North Atlantic sea surface temperature (NASST) are presented. The authors target in particular the 1940–75 “warm-to-cold” transition, an event that is generally framed in the context of the longer-term Atlantic multidecadal variability (AMV) cycle, in turn associated with the Atlantic meridional overturning circulation (AMOC) internal variability. Here the authors examine the ability of uninitialized, historical integrations from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) archive to retrospectively reproduce this specific episode of twentieth-century climatic history, under a hierarchy of forcing conditions. For this purpose, both standard and so-called historical Misc CMIP5 simulations of the historical climate (combining selected natural and anthropogenic forcings) are exploited. Based on this multimodel analysis, evidence is found for a significant influence of anthropogenic agents on multidecadal sea surface temperature (SST) fluctuations across the Atlantic sector, suggesting that anthropogenic aerosols and greenhouse gases might have played a key role in the 1940–75 North Atlantic cooling. However, the diagnosed forced response in CMIP5 models appears to be affected by a large uncertainty, with only a limited subset of models displaying significant skill in reproducing the mid-twentieth-century NASST cooling. Such uncertainty originates from the existence of well-defined behavioral clusters within the analyzed CMIP5 ensembles, with the bulk of the models splitting into two main clusters. Such a strong polarization calls for some caution when using a multimodel ensemble mean in climate model analyses, as averaging across fairly distinct model populations may result, through mutual cancellation, in a rather artificial description of the actual multimodel ensemble behavior.

A potentially important role for both anthropogenic aerosols and greenhouse gases with regard to the observed North Atlantic multidecadal variability has clear implications for decadal predictability and predictions. The uncertainty associated with alternative aerosol and greenhouse gas emission scenarios should be duly accounted for in designing a common protocol for coordinated decadal forecast experiments.

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M. Hoerling, J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, S. Schubert, and R. Seager

Central Great Plains precipitation deficits during May–August 2012 were the most severe since at least 1895, eclipsing the Dust Bowl summers of 1934 and 1936. Drought developed suddenly in May, following near-normal precipitation during winter and early spring. Its proximate causes were a reduction in atmospheric moisture transport into the Great Plains from the Gulf of Mexico. Processes that generally provide air mass lift and condensation were mostly absent, including a lack of frontal cyclones in late spring followed by suppressed deep convection in the summer owing to large-scale subsidence and atmospheric stabilization.

Seasonal forecasts did not predict the summer 2012 central Great Plains drought development, which therefore arrived without early warning. Climate simulations and empirical analysis suggest that ocean surface temperatures together with changes in greenhouse gases did not induce a substantial reduction in sum mertime precipitation over the central Great Plains during 2012. Yet, diagnosis of the retrospective climate simulations also reveals a regime shift toward warmer and drier summertime Great Plains conditions during the recent decade, most probably due to natural decadal variability. As a consequence, the probability of the severe summer Great Plains drought occurring may have increased in the last decade compared to the 1980s and 1990s, and the so-called tail risk for severe drought may have been heightened in summer 2012. Such an extreme drought event was nonetheless still found to be a rare occurrence within the spread of 2012 climate model simulations. The implications of this study's findings for U.S. seasonal drought forecasting are discussed.

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R. Dragani, G. Redaelli, G. Visconti, A. Mariotti, V. Rudakov, A. R. MacKenzie, and L. Stefanutti

Abstract

Numerical experiments and statistical analyses are conducted to determine the skill of different Lagrangian techniques for the construction of tracer distributions. High-resolution potential vorticity (PV) maps are calculated from simulations of the 1996/97 arctic winter stratospheric dynamics using two different numerical schemes—reverse domain filling trajectories (RDF) and contour advection with surgery (CAS)—and data from three meteorological agencies (NCEP, the Met Office, and ECMWF). The PV values are then converted into ozone (O3) concentrations and statistically compared to in situ O3 data measured by the electro chemical ozone cell (ECOC) instrument during the Airborne Polar Experiment (APE) using cross correlation, rms differences, and the Kolmogorov–Smirnov (KS) test.

Results indicate that while Lagrangian techniques are successful in increasing the presence of lower-scale tracer structures with respect to the plain meteorological analyses, they significantly improve the statistical agreement between the simulated and the measured tracer profiles only when there is clear evidence of filaments in the measured data. This better fit is most clearly seen by using the KS test, rather than cross correlation. It is argued that this difference in the performance of Lagrangian techniques can be partly related to the treatment of mixing processes in the framework of the Lagrangian schemes. Statistical analyses also show that the temporal rather than the spatial resolution of the input meteorological fields, used to advect tracers, enhances the predictive skill of the Lagrangian products. The best overall performance is obtained with the Lagrangian product (not gridded) based on high-resolution reverse trajectories calculated along a flight track, in particular when the simulation is initialized with ECMWF data. Other products, such as CAS initialized with ECMWF and 3D-gridded RDF initialized with the Met Office data, show fairly good performances, thus with lower statistical confidence.

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M. Zhang, A. Mariotti, Z. Lin, V. Ramasmamy, J. Lamarque, Z. Xie, and J. Zhu
Open access
Annarita Mariotti, Cory Baggett, Elizabeth A. Barnes, Emily Becker, Amy Butler, Dan C. Collins, Paul A. Dirmeyer, Laura Ferranti, Nathaniel C. Johnson, Jeanine Jones, Ben P. Kirtman, Andrea L. Lang, Andrea Molod, Matthew Newman, Andrew W. Robertson, Siegfried Schubert, Duane E. Waliser, and John Albers

Abstract

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Free access
P. Drobinski, V. Ducrocq, P. Alpert, E. Anagnostou, K. Béranger, M. Borga, I. Braud, A. Chanzy, S. Davolio, G. Delrieu, C. Estournel, N. Filali Boubrahmi, J. Font, V. Grubišić, S. Gualdi, V. Homar, B. Ivančan-Picek, C. Kottmeier, V. Kotroni, K. Lagouvardos, P. Lionello, M. C. Llasat, W. Ludwig, C. Lutoff, A. Mariotti, E. Richard, R. Romero, R. Rotunno, O. Roussot, I. Ruin, S. Somot, I. Taupier-Letage, J. Tintore, R. Uijlenhoet, and H. Wernli

The Mediterranean countries are experiencing important challenges related to the water cycle, including water shortages and floods, extreme winds, and ice/snow storms, that impact critically the socioeconomic vitality in the area (causing damage to property, threatening lives, affecting the energy and transportation sectors, etc.). There are gaps in our understanding of the Mediterranean water cycle and its dynamics that include the variability of the Mediterranean Sea water budget and its feedback on the variability of the continental precipitation through air–sea interactions, the impact of precipitation variability on aquifer recharge, river discharge, and soil water content and vegetation characteristics specific to the Mediterranean basin and the mechanisms that control the location and intensity of heavy precipitating systems that often produce floods. The Hydrological Cycle in Mediterranean Experiment (HyMeX) program is a 10-yr concerted experimental effort at the international level that aims to advance the scientific knowledge of the water cycle variability in all compartments (land, sea, and atmosphere) and at various time and spatial scales. It also aims to improve the processes-based models needed for forecasting hydrometeorological extremes and the models of the regional climate system for predicting regional climate variability and evolution. Finally, it aims to assess the social and economic vulnerability to hydrometeorological natural hazards in the Mediterranean and the adaptation capacity of the territories and populations therein to provide support to policy makers to cope with water-related problems under the influence of climate change, by linking scientific outcomes with related policy requirements.

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Eric D. Maloney, Andrew Gettelman, Yi Ming, J. David Neelin, Daniel Barrie, Annarita Mariotti, C.-C. Chen, Danielle R. B. Coleman, Yi-Hung Kuo, Bohar Singh, H. Annamalai, Alexis Berg, James F. Booth, Suzana J. Camargo, Aiguo Dai, Alex Gonzalez, Jan Hafner, Xianan Jiang, Xianwen Jing, Daehyun Kim, Arun Kumar, Yumin Moon, Catherine M. Naud, Adam H. Sobel, Kentaroh Suzuki, Fuchang Wang, Junhong Wang, Allison A. Wing, Xiaobiao Xu, and Ming Zhao

Abstract

Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers’ model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.

Open access