Javier García-Serrano

Initialized near-term regional climate change prediction

Climate models are seen by many to be unverifiable. However, near-term climate predictions up to 10 years into the future carried out recently with these models can be rigorously verified against observations. Near-term climate prediction is a new information tool for the climate adaptation and service communities, which often make decisions on near-term time scales, and for which the most basic information is unfortunately very scarce. The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here. We show that climate forecast systems have skill in predicting the Earths temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions. Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.

Tropical Atlantic Variability Modes (1979-2002). Part I: Time-Evolving SST Modes Related to West African Rainfall

Abstract This work presents a description of the 1979–2002 tropical Atlantic (TA) SST variability modes coupled to the anomalous West African (WA) rainfall during the monsoon season. The time-evolving SST patterns, with an impact on WA rainfall variability, are analyzed using a new methodology based on maximum covariance analysis. The enhanced Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset, which includes measures over the ocean, gives a complete picture of the interannual WA rainfall patterns for the Sahel dry period. The leading TA SST pattern, related to the Atlantic El Nino, is coupled to anomalous precipitation over the coast of the Gulf of Guinea, which corresponds to the second WA rainfall principal component. The thermodynamics and dynamics involved in the generation, development, and damping of this mode are studied and compared with previous works. The SST mode starts at the Angola/Benguela region and is caused by alongshore wind anomalies. It then propagates wes...

Sensitivity of decadal predictions to the initial atmospheric and oceanic perturbations

A coupled global atmosphere–ocean model is employed to investigate the impact of initial perturbation methods on the behaviour of five-member ensemble decadal re-forecasts. Three initial-condition perturbation strategies, atmosphere only, ocean only and atmosphere–ocean, have been used and the impact on selected variables have been investigated. The impact has been assessed in terms of climate drift, forecast quality and spread. The simulated global means of near-surface air temperature (T2M), sea surface temperature (SST) and sea ice area (SIA) for both Arctic and Antarctic show reasonably good quality, in spite of the non-negligible drift of the model. The skill in terms of correlation is not significantly affected by the particular perturbation method employed. The ensemble spread generated for T2M, SST and land surface precipitation (PCP) saturates quickly with any of the perturbation methods. However, for SIA, Atlantic meridional overturning circulation (AMOC) and ocean heat content (OHC), the spread increases substantially during the forecast time when ocean perturbations are applied. Ocean perturbations are particularly important for Antarctic SIA and OHC for the middle and deep layers of the ocean. The results will be helpful in the design of ensemble prediction experiments.

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.

Variability and Predictability of West African Droughts: A Review on the Role of Sea Surface Temperature Anomalies

AbstractThe Sahel experienced a severe drought during the 1970s and 1980s after wet periods in the 1950s and 1960s. Although rainfall partially recovered since the 1990s, the drought had devastating impacts on society. Most studies agree that this dry period resulted primarily from remote effects of sea surface temperature (SST) anomalies amplified by local land surface–atmosphere interactions. This paper reviews advances made during the last decade to better understand the impact of global SST variability on West African rainfall at interannual to decadal time scales. At interannual time scales, a warming of the equatorial Atlantic and Pacific/Indian Oceans results in rainfall reduction over the Sahel, and positive SST anomalies over the Mediterranean Sea tend to be associated with increased rainfall. At decadal time scales, warming over the tropics leads to drought over the Sahel, whereas warming over the North Atlantic promotes increased rainfall. Prediction systems have evolved from seasonal to decada...

The Indian Ocean: The Region of Highest Skill Worldwide in Decadal Climate Prediction*

AbstractThe Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts tak...

On the Predictability of the Winter Euro-Atlantic Climate: Lagged Influence of Autumn Arctic Sea Ice

AbstractSatellite-derived sea ice concentration (SIC) and reanalyzed atmospheric data are used to explore the predictability of the winter Euro-Atlantic climate resulting from autumn SIC variability over the Barents–Kara Seas region (SIC/BK). The period of study is 1979/80–2012/13. Maximum covariance analyses show that the leading predictand is indistinguishable from the North Atlantic Oscillation (NAO). The leading covariability mode between September SIC/BK and winter North Atlantic–European sea level pressure (SLP) is not significant, indicating that no empirical prediction skill can be achieved. The leading covariability mode with either October or November SIC/BK is moderately significant (significance levels <10%), and both predictor fields yield a cross-validated NAO correlation of 0.3, suggesting some empirical prediction skill of the winter NAO index, with sea ice reduction in the Barents–Kara Seas being accompanied by a negative NAO phase in winter. However, only November SIC/BK provides signifi...

Tropical Atlantic Variability Modes (1979–2002). Part II: Time-Evolving Atmospheric Circulation Related to SST-Forced Tropical Convection

Abstract The ways in which deep convection over the tropical Atlantic affects the midlatitude climate variability through meridional circulation, planetary wave teleconnection, and wave–mean flow interaction is examined for the 1979–2002 period, by following the North Atlantic anomalous rainfall evolution from summer to late winter. In this way, the first two covariability modes between anomalous summer tropical Atlantic sea surface temperature (SST) and anomalous summer–late-winter precipitation over the North Atlantic basin are analyzed using the same methodology of extended maximum covariance analysis developed for Part I. This work updates the results given by other authors, whose studies are based on different datasets dating back to the 1950s. To this end, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset, which includes measures over the ocean, is used to give a complete picture of the interannual rainfall patterns for the last decades. The first mode, which accoun...

Decadal prediction skill in the ocean with surface nudging in the IPSL-CM5A-LR climate model

Two decadal prediction ensembles, based on the same climate model (IPSL-CM5A-LR) and the same surface nudging initialization strategy are analyzed and compared with a focus on upper-ocean variables in different regions of the globe. One ensemble consists of 3-member hindcasts launched every year since 1961 while the other ensemble benefits from 9 members but with start dates only every 5 years. Analysis includes anomaly correlation coefficients and root mean square errors computed against several reanalysis and gridded observational fields, as well as against the nudged simulation used to produce the hindcasts initial conditions. The last skill measure gives an upper limit of the predictability horizon one can expect in the forecast system, while the comparison with different datasets highlights uncertainty when assessing the actual skill. Results provide a potential prediction skill (verification against the nudged simulation) beyond the linear trend of the order of 10 years ahead at the global scale, but essentially associated with non-linear radiative forcings, in particular from volcanoes. At regional scale, we obtain 1 year in the tropical band, 10 years at midlatitudes in the North Atlantic and North Pacific, and 5 years at tropical latitudes in the North Atlantic, for both sea surface temperature (SST) and upper-ocean heat content. Actual prediction skill (verified against observational or reanalysis data) is overall more limited and less robust. Even so, large actual skill is found in the extratropical North Atlantic for SST and in the tropical to subtropical North Pacific for upper-ocean heat content. Results are analyzed with respect to the specific dynamics of the model and the way it is influenced by the nudging. The interplay between initialization and internal modes of variability is also analyzed for sea surface salinity. The study illustrates the importance of two key ingredients both necessary for the success of future coordinated decadal prediction exercises, a high frequency of start dates is needed to achieve robust statistical significance, and a large ensemble size is required to increase the signal to noise ratio.

The Influence of Autumnal Eurasian Snow Cover on Climate and Its Link with Arctic Sea Ice Cover

AbstractThe relationship between Eurasian snow cover extent (SCE) and Northern Hemisphere atmospheric circulation is studied in reanalysis during 1979–2014 and in CMIP5 preindustrial control runs. In observations, dipolar SCE anomalies in November, with negative anomalies over eastern Europe and positive anomalies over eastern Siberia, are followed by a negative phase of the Arctic Oscillation (AO) one and two months later. In models, this effect is largely underestimated, but four models simulate such a relationship. In observations and these models, the SCE influence is primarily due to the eastern Siberian pole, which is itself driven by the Scandinavian pattern (SCA), with a large anticyclonic anomaly over the Urals. The SCA is also responsible for a link between Eurasian SCE anomalies and sea ice concentration (SIC) anomalies in the Barents–Kara Sea.Increasing SCE over Siberia leads to a local cooling of the lower troposphere and is associated with warm conditions over the eastern Arctic. This is fol...