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Decadal climate prediction and predictability for climate services

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Universitat de Barcelona. Facultat de Física

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[eng] Climate variations at annual to decadal time scales affect many regions around the globe,
causing direct impacts on the economy, ecosystems and society. Knowing these variations in
advance allows for implementing measures to adapt, mitigate and build resilience to the
consequences of a changing climate. At decadal time scale, climate variations are caused
by both external forcings and internal climate variability. While climate projections
incorporate external forcing information based on different socio-economic scenarios to
project possible pathways the climate system would follow, decadal predictions also
incorporate information on the current climate state through a model initialisation procedure.
Forecast quality assessment, which involves comparing hindcasts (retrospective predictions)
to past observations to evaluate their degree of agreement, is an essential step to ensure
that such predictions are trustable and beneficial for decision-making. Climate hindcasts also
allow for applying post-processing techniques such as calibration and downscaling methods,
as well as for selecting the best climate information for each specific variable, region and
forecast period.
This Ph.D. thesis has focused on evaluating the forecast quality of variables and indicators
relevant for decision-making in several sectors, with a particular focus on agriculture. The
evaluation has been performed globally, for individual models and multi-model ensemble,
and different forecast periods to identify windows of opportunity for which the climate
predictions show skill to support decision-making. Furthermore, historical forcing simulations
(retrospective climate projections) have been used to estimate the impact of initialisation.
First, the quality of multi-model forecasts of temperature, precipitation, the Atlantic
Multidecadal Variability index (AMV) and Global near-Surface Air Temperature (GSAT)
generated from all decadal hindcasts contributing to the Coupled Model Intercomparison
Project Phase 6 (CMIP6) were evaluated, finding high skill for predictions of temperature,
AMV and GSAT, and limited skill for predictions of precipitation. The multi-model ensemble
was also compared to individual forecast systems, finding that the best system for each
particular location usually outperforms the multi-model ensemble. However, the multi-model
provides higher skill than at least half of the systems. The decadal predictions were also
compared to the historical simulations, finding an added value from model initialisation over
several ocean and land regions for temperature, and for AMV and GSAT. The full
multi-model was compared to a sub-ensemble of predictions generated from forecast
systems that provided timely forecasts to assess the impact of the ensemble size in an
operational climate services context.
Second, the representation and prediction of the Euro-Atlantic weather regimes by the
EC-Earth3 model were assessed by identifying the dominating atmospheric circulation
patterns in this region. The Euro-Atlantic weather regimes are the positive and negative
phases of the North Atlantic Oscillation (NAO+ and NAO-, respectively), Blocking, and
Atlantic Ridge in winter; and the NAO-, Blocking, Atlantic Ridge, and Atlantic Low in summer.
The EC-Earth3 correctly represents the spatial patterns and climatological frequencies of all
weather regimes. However, the skill in predicting the annual to decadal variations of the
weather regimes’ frequency of occurrence is low, and the model initialisation does not
improve such prediction skill.
Third, the multi-model forecast quality of the CMIP6 decadal hindcasts is evaluated for
multi-annual predictions of a set of indices related to the frequency and intensity of daily
temperature and precipitation extremes. The multi-model ensemble is skillful in predicting
temperature extremes over most land regions, while the quality is lower for precipitation
extremes. Comparing the skill with that for mean temperature and precipitation, extremes
are predicted with lower skill, especially those related to the most extreme days. Compared
to the historical forcing simulations, decadal predictions show only small and
region-dependent skill improvements from model initialisation.
Finally, this Ph.D. thesis presents the applications of the research within several European
projects and a contract with a private company for which prototypes of climate services have
been created. For instance, prototypes of forecast products have been developed for the
Southern African Development Community region. These prototypes consist of annual

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[spa] Las variaciones climáticas de uno a diez años impactan a la economía, ecosistemas y
sociedad. Anticipar dichas variaciones permite implementar medidas de adaptación y
mitigación a consecuencias de un clima variable. La variabilidad decadal está causada tanto
por forzamientos externos como por variabilidad interna. Los modelos climáticos permiten
estudiar la dinámica y anticipar las variaciones. Mientras que las proyecciones climáticas
incorporan forzamientos externos basados en distintos escenarios socioeconómicos, las
predicciones decadales también incorporan información del estado actual del sistema
climático a través del proceso de inicialización del modelo.
Esta tesis doctoral se centra en evaluar la calidad de las predicciones comparándolas con
observaciones del pasado con el objetivo de asegurar que son útiles y beneficiosas para la
toma de decisiones. Esta evaluación permite identificar variables, regiones y periodos en los
cuales la calidad de la información climática se puede utilizar para llevar a cabo decisiones.
También, se han aplicado técnicas de postprocesado (calibración, multi-modelo y
regionalización) con el fin de mejorar las predicciones y adaptarlas a las necesidades de los
usuarios. Las predicciones se han comparado con las simulaciones de forzamiento histórico
con el fin de estimar el impacto de la inicialización del modelo en la calidad de las
predicciones.
La tesis consta de tres publicaciones científicas en peer-reviewed revistas. El primer estudio
se centra en la evaluación de predicciones multi-modelo para temperatura, precipitación,
Atlantic Multidecadal Variability index (AMV) y temperatura global. También se estima el
beneficio del uso del multi-modelo en lugar de modelos individuales, impacto de la
inicialización, impacto de calibración, e impacto del número de modelos. El segundo estudio
evalúa la representación espacial y predictibilidad temporal de tipos de tiempo europeos
(por ejemplo, el bloqueo). El tercer estudio se enfoca a la predicción de extremos climáticos
de temperatura y precipitación dada su importancia en la sociedad y sectores vulnerables a
las variaciones climáticas. Finalmente, se muestran ejemplos de aplicaciones de la
investigación llevada a cabo junto con prototipos de servicios climáticos, con particular
enfoque a la agricultura.

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Decadal climate prediction and predictability for climate services