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A data driven method based on generalized additive modelling (GAM) has been developed for homogenizing daily minimum and maximum temperature (TN, TX) series using parallel measurements and covariates. The method is applied to two coastal and two inland stations in the Netherlands. Between 1950 and 1972, these stations were relocated from cities to airports, accompanied by parallel measurement of at least 5 years at the old and new sites. Separating these parallel measurements in training and test data, the method compares numerous models involving covariates like the wind vector, cloudiness, specific humidity and sea surface temperature, and selects a model for each station. The resulting models offer an improvement compared to models based on temperature and season only: seasonal dependence is largely replaced by dependence on physical quantities. However, quantitatively, the impact is not large in the cases studied. One of the reasons might be that some covariates have only been measured at specific times not coinciding with the occurrences of the temperature minima or maxima. Additional benefits of the method are robustness and estimation of the sampling error variance of the daily homogenized daily temperature values. [ABSTRACT FROM AUTHOR]

The global acceleration of sea-level rise (SLR) during the 20th century is now established. On the local scale, this is harder to establish as several drivers of SLR play a role, which can mask the acceleration. Here, we study the rate of SLR along the coast of the Netherlands from the average of six tide gauge records covering the period 1890–2021. To isolate the effects of the wind field variations and the nodal tide from the local sea-level trend, we use four generalised additive models (GAMs) which include different predictive variables. From the sea-level trend estimates, we obtain the continuous evolution of the rate of SLR and its uncertainty over the observational period. The standard error in the estimation of the rate of SLR is reduced when we account for nodal-tide effects and is reduced further when we also account for the wind effects, meaning these provide better estimates of the rate of SLR. A part of the long-term SLR is due to wind forcing related to a strengthening and northward shift of the jet stream, but this SLR contribution decelerated over the observational period. Additionally, we detect wind-forced sea-level variability on multidecadal timescales with an amplitude of around 1 cm. Using a coherence analysis, we identify both the North Atlantic Oscillation and the Atlantic Multidecadal Variability as its drivers. Crucially, accounting for the nodal-tide and wind effects changes the estimated rate of SLR, unmasking an SLR acceleration that started in the 1960s. Our best-fitting GAM, which accounts for nodal and wind effects, yields a rate of SLR of about 1.72.21.3 mm yr -1 in 1900–1919 and 1.51.91.2 mm yr -1 in 1940–1959 compared to 2.93.52.4 mm yr -1 in 2000–2019 (where the lower and upper bounds denote the 5th and 95th percentiles). If we discount the nodal tide, wind and fluctuation effects and assume a constant rate of SLR, then the probability (p value) of finding a rate difference between 1940–1959 and 2000–2019 of at least our estimate is smaller than 1 %. Consistent with global observations and the expectations based on the physics of global warming, our results show unequivocally that SLR along the Dutch coast has accelerated since the 1960s. [ABSTRACT FROM AUTHOR]

While a global acceleration of sea-level rise (SLR) during the 20th century is now established, locally acceleration is more difficult to detect because additional processes play a role which sometimes mask the acceleration. Here we study the rate of SLR along the coast of the Netherlands from six tide gauge records, covering the period 1890-2000. We focus on the influence of the wind field and the nodal tide variations on the local sea-level trend. We use four generalised additive models, including different predictive variables, and a parametric bootstrap method to compute the sea-level trend. From the sea-level trend, we obtain the continuous evolution of the rate of SLR and its uncertainty over the observational period through differentiation. Accounting for the nodal cycle only or both the nodal cycle and the wind influence on sea level reduces the standard error in the estimation of the rate of SLR. Moreover, accounting for both the nodal and wind influence changes the estimated rate of SLR, unmasking an acceleration of SLR that started in the 1960s. Our best-fitting statistical model yields a rate of SLR of about 1.8 [1.4-2.3] mm/yr in 1900-1919 and 1.5 [1.1-1.8] mm/yr in 1940-1959 compared to 3.0 [2.4-3.5] mm/yr over 2000-2019. If, apart from tidal, wind effects and fluctuations, sea level would have increased at a constant rate, then the probability (the p-value) of finding a rate difference between 1940-1959 and 2000-2019 of at least our estimate is smaller than 1 %. Our findings can be interpreted as an unequivocal sign of the acceleration of current SLR along the Dutch coast since the 1960s. This aligns with global SLR observations and expectations based on a physical understanding of SLR related to global warming. A small but significant part of the long-term sea-level trend is due to wind forcing related to a strengthening and northward shift of the jet stream. Additionally, we detect a multidecadal mode of sea-level variability forced by the wind with an amplitude of around 1 cm. We argue that it is related to multi-decadal sea surface temperature variations in the North Atlantic, similar to the Atlantic Multidecadal Variability. [ABSTRACT FROM AUTHOR]