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We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960–2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. Further improvements of the simulated mean state of ICON-ESM, e.g. through fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean, are expected to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.
We develop a data assimilation scheme with the Icosahedral Non-hydrostatic Earth System Model (ICON-ESM) for operational decadal and seasonal climate predictions at the German weather service. For this purpose, we implement an Ensemble Kalman Filter to the ocean component as a first step towards a weakly coupled data assimilation. We performed an assimilation experiment over the period 1960–2014. This ocean-only assimilation experiment serves to initialize 10-year long retrospective predictions (hindcasts) started each year on 1 November. On multi-annual time scales, we find predictability of sea surface temperature and salinity as well as oceanic heat and salt contents especially in the North Atlantic. The mean Atlantic Meridional Overturning Circulation is realistic and the variability is stable during the assimilation. On seasonal time scales, we find high predictive skill in the tropics with highest values in variables related to the El Niño/Southern Oscillation phenomenon. In the Arctic, the hindcasts correctly represent the decreasing sea ice trend in winter and, to a lesser degree, also in summer, although sea ice concentration is generally much too low in both hemispheres in summer. However, compared to other prediction systems, prediction skill is relatively low in regions apart from the tropical Pacific due to the missing atmospheric assimilation. Further improvements of the simulated mean state of ICON-ESM, e.g. through fine-tuning of the sea ice and the oceanic circulation in the Southern Ocean, are expected to improve the predictive skill. In general, we demonstrate that our data assimilation method is successfully initializing the oceanic component of the climate system.
Accurate representation of the Atlantic Meridional Overturning Circulation (AMOC) in global climate models is crucial for reliable future climate predictions and projections. In this study, we used 42 coupled atmosphere–ocean global climate models to analyze low-frequency variability of the AMOC driven by the North Atlantic Oscillation (NAO). Our results showed that the influence of the simulated NAO on the AMOC differs significantly between the models. We showed that the large intermodel diversity originates from the diverse oceanic mean state, especially over the subpolar North Atlantic (SPNA), where deep water formation of the AMOC occurs. For some models, the climatological sea ice extent covers a wide area of the SPNA and restrains efficient air–sea interactions, making the AMOC less sensitive to the NAO. In the models without the sea-ice-covered SPNA, the upper-ocean mean stratification critically affects the relationship between the NAO and AMOC by regulating the AMOC sensitivity to surface buoyancy forcing. Our results pinpoint the oceanic mean state as an aspect of climate model simulations that must be improved for an accurate understanding of the AMOC.
One of the effects of climate change is the rise of sea level, which poses an important threat to coastal areas. Therefore, the protection and management of coastal ecosystems as well as human infrastructures and constructions require an accurate knowledge of those changes occurring at a local scale. In this study, long time series of sea level from tide gauges distributed along the southern (Atlantic) and eastern (Mediterranean) Spanish coasts were analyzed. Linear trends were calculated for two periods, from early 1940s to 2018 and from 1990 to 2018. Values for the former period ranged between 0.68 and 1.22 mm/year. These trends experienced a significant increase for the second period, when they ranged between 1.5 and 4.6 mm/year. Previous research analyzed the effect of atmospheric forcing in the Mediterranean Sea by means of 2D numerical models, and the steric contribution was directly evaluated by the integration of density along the water column. In this study, the effect of atmospheric forcing and the thermosteric and halosteric contributions on coastal sea level were empirically determined by means of statistical linear models that established which factors affected sea level at each location and what the numerical response of the observed sea level was to the contributing factors. Atmospheric pressure and the west–east component of the wind hada significant contribution to the sea level variability at most of the tide gauges. The thermosteric and halosteric components of sea level also contributed to the sea level variability at all the tide gauges, with the only exception of Alicante. Atmospheric forcing and the steric components of sea level experienced long-term trends. The combination of such trends, with the response of sea level to these factors, allowed us to estimate their contribution to the observed sea level trends. The part of these trends not explained by the atmospheric variables and the steric contributions was attributed to mass addition. Trends associated with mass addition ranged between 0.6 and 1.2 mm/year for the period 1948–2018 and between 1.0 and 4.5 mm/year for the period 1990–2018.
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