Search

Recent studies have suggested that the Atlantic water pathway connecting the subpolar North Atlantic (SPNA) with the Nordic Seas and Arctic Ocean may lead to skillful predictions of sea surface temperature and salinity anomalies in the eastern Nordic Seas. To investigate the role of the SPNA for such anomalies downstream, we designed a pacemaker experiment, using two decadal climate prediction systems based on EC-Earth3 and NorCPM. We focus on the subpolar extreme cold anomaly in 2015 and its subsequent development, a feature not well captured and predicted. The pacemaker experiment follows the protocol of the CMIP6 DCPP-A retrospective forecasts or hindcasts initialized November 1, 2014, but the models are forced to follow the observed ocean temperature and salinity anomalies in the SPNA from ocean reanalysis from November 2014 through to December 2019. Two sets of 10-year hindcasts are performed with 10 members for EC-Earth3 and 30 members for NorCPM. We here detail and discuss the design of this pacemaker experiment and present results, comparing with the initialized CMIP6 DCPP-A experiment assessing differences in decadal prediction skill outside the SPNA. We conclude that the pacemaker experiments show improved skill compared to the standard decadal predictions for the eastern Norwegian Sea, and therefore the SPNA is key for successful decadal predictions in the region.

Extreme precipitation events can lead to severe negative consequences on society, the economy, and the environment. To mitigate related risks, it is crucial to understand their natural causes. There is a vast number of methods in the literature analyzing their connection to large-scale drivers. Recently there has been much interest in using machine learning (ML) methods instead of traditional statistical models like regression. ML methods are based on algorithms adapting and learning from data. By contrast, regression models are based on theory and assumptions and benefit from domain knowledge for model specification. Because of its adaptability, ML is claimed to offer superior predictive performance than traditional statistical modeling and better manage a greater number of potential predictors. A few studies in climate research have compared the performance between these two approaches, but their conclusions are inconsistent, and some have limitations. We used five predictor variables - Geopotential height at 500hPA, Convective available energy (CAPE), Total column water (TCW), Sea Surface Temperature (SST), and Surface Temperature (SAT) using ERA5, the latest reanalysis dataset from ECMWF, and data produced by the Danish Meteorological Institute. All the predictors were not used directly as inputs but were preprocessed before modeling. We trained models using logistic regression (LR) and three commonly used supervised machine learning algorithms - random forests (RF), neural networks (NNET), and support vector machines (SVM) to predict whether an extreme event occurred over Copenhagen. In the LR framework, the predictor variables were modeled using restricted cubic splines to address potential nonlinearity. The training data are highly unbalanced, so using a traditional performance metric such as accuracy (ACC) could be misleading. In light of this, we use performance metrics specialized for unbalanced datasets: the ROC (receiver operating characteristic) curve as the primary measure and the area under the precision-recall curve, the Brier score, and ACC together with the true positive rate and the false positive rate at the optimal threshold as secondary measures.During the variable selection process, it was found that SST has the weakest relationship with extreme events, and its inclusion did not increase the model performance. Furthermore, the results showed that the LR performs similarly to more complex ML algorithms. SVM had the worst performance in all cases. While most of the top-ranked impacting predictors were nearly comparable amongst models, especially CAPE and TCW, we found discrepancies; SAT contributed to RF and NNET but not to LR.

Responding to the challenges of a changing climate requires information that is relevant and actionable at the local scale where adaptation actions take place. Here we describe the development of Klimaatlas, the Danish National Climate Atlas, a tool to provide information to ministries, regional authorities, businesses and citizens about climate change in Denmark. We focus in particular on the key decisions that needed to be made during development and the lessons learnt from doing so that are relevant to the development of similar tools in other jurisdictions. We describe the user groups that we involved in the development of the atlas, how their interactions shaped the final product, and the ongoing role of the user in the project. Communication and dissemination of results was approached on three different levels simultaneously, and we describe this approach, and its benefits, in detail. We detail some of the major technical challenges faced, particularly around the choice of bias-correction methods, and how these issues were overcome. The approach to quantifying and communicating uncertainty, and its importance, also receives particular attention. We assess the impact of Klimaatlas, and the challenges around quantifying its usefulness, together with the results of a recent user survey identifying the strengths and weaknesses. We also place Klimaatlas in the context of other comparable products, and particularly the recently developed IPCC Interactive Atlas. Finally, we discuss issues around future maintenance and possible expansions of Klimaatlas, including the use of convection permitting simulations, incorporation of compound events, updates between IPCC cycles and extensions to new sectors.

Climate models usually can not afford to include an interactive ice sheet component for Greenland, which leads to a wrong representation of the variability of the freshwater fluxes released from the Greenland ice melt into the North Atlantic. We propose here to force externally a climate model (EC-Earth3) over several decades (1920-2014) with an observational dataset of runoff and solid ice discharge values for Greenland and surrounding glaciers and ice caps. It has been shown in a similar study with the IPSL-CM6-LR model that an enhancement of freshwater can modify the circulation and the convection in this region. The simulated mixed layer depths in the Nordic seas and the strength of the Atlantic Meridional Overturning Circulation will be investigated to assess the impact of these increasing freshwater fluxes on the oceanic circulation over the period. The response in salinity and stratification in the Arctic will also be analysed as well as the ability for the system to capture abrupt changes like the 1995 warming in the subpolar gyre.

The North Atlantic Oscillation (NAO) is the leading mode of atmospheric variability during winter in the North Atlantic region on time scales ranging from ~5-7 days over months to the entire winter season. It influences the weather and climate in the entire North Atlantic-European region and even beyond and has been recognised for centuries. It is scientifically characterised by the NAO index, which is essentially the pressure difference between the Azores and Iceland, in combination with a spatial pressure anomaly pattern. Often the NAO is regarded as consisting of positive or negative events with the duration of around one week with intermittent periods of near-neutral conditions, and the net number of events combined with their magnitude then form the NAO strength for the winter. Here we wish to point out that this picture appears too simplified. We find that the phase of the NAO outside the time horizon of one week has an influence on the shorter NAO type events, most pronounced for NAO- events where the influence can both distort and at the same time either amplify or diminish the NAO pattern. This has potential implications for seasonal or longer time-scale predictability in our region. In a similar manner, the well-known strong correlation between the phase and strength of the NAO with precipitation anomalies over Europe may be also skewed. We identify that on a monthly to seasonal time scale the correlation may be less significant for strong negative precipitation anomalies than appears to be the case for strong positive anomalies. In some of the examined data, specifically dry conditions in north-western Europe do not seem to show a clear connection to a particular phase of the NAO and thus cannot necessarily be used to derive information about the state of this atmospheric circulation pattern, an approach often taken when trying to reconstruct the atmospheric circulation based on proxy records.

The subpolar North Atlantic (SPNA) is a region experiencing substantial decadal variability, which has been linked to extreme weather impacts over continents. Recent studies have suggested that the connectivity with the SPNA may be a key to predictions in high latitudes. To understand the impact of the SPNA on predictability of North Atlantic-European sectors and the Arctic, we use two climate prediction systems, EC-Earth3-CPSAI and NorCPM1, to perform ensemble pacemaker experiments with a focus on the subpolar extreme cold anomaly event in 2015. This 2015 cold anomaly event is generally underestimated by the decadal prediction systems. In order to force the model to better represent the observed anomaly in SPNA, we apply nudging in a region of the SPNA (i.e., 51.5°W - 13.0°W, 30.4°N - 57.5°N, and from surface to 1000 m depth in the ocean). Here ocean temperature and salinity is restored to observed conditions from reanalysis in both model systems. All other aspects of the setup of this pacemaker experiment follow the protocol for the CMIP6 DCPP-A hindcasts and initialized on November 1, 2014. The restoration is applied during the hindcasts from November 2014 to December 2019. Multi-member ensembles of 10-year hindcasts are performed with 10 members for the EC-Earth3-CPSAI and 30 members for the NorCPM1.The time evolution of ensembles of the initialized nudging hindcasts (EXP1) is compared with the initialized DCPP-A hindcast ensembles (EXP2) and the uninitialized ensembles (EXP3). The prediction skills of the three sets of experiments are also assessed. It can be seen that restoring the ocean temperature and salinity in the SPNA region to the reanalysis improves the prediction in the region quickly after the simulation starts, as expected. On the interannual to decadal time scales, the areas with improved prediction skills extend to over almost the entire North Atlantic for both models. The improved skill over Nordic Seas is particularly significant, especially for EC-Earth3-CPSAI. For NorCPM, the regions with improved skills extend to the entire Arctic. Our results suggest the possible role of the SPNA as a source of skillful predictions on interannual to decadal time scale, especially for high latitudes. The ocean pathways are the critical source of skill whereas our results imply a limited role of coupled feedbacks through the atmosphere.

The Earth System Model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different HPC systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behaviour and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.

We study the stability of estimated linear statistical relations of global mean temperature and global mean sea level with regard to data revisions. Using four different model specifications proposed in the literature, we compare coefficient estimates and long-term sea level projections using two different vintages of each of the annual time series, covering the periods 1880&ndash, 2001 and 1880&ndash, 2013. We find that temperature and sea level updates and revisions have a substantial influence both on the magnitude of the estimated coefficients of influence (differences of up to 50%) and therefore on long-term projections of sea level rise following the RCP4.5 and RCP6 scenarios (differences of up to 40 cm by the year 2100). This shows that in order to replicate earlier results that informed the scientific discussion and motivated policy recommendations, it is crucial to have access to and to work with the data vintages used at the time.

Severe precipitation events occur rarely and are often localized in space and of short duration; but they are important for societal managing of infrastructure. Therefore, there is a demand for estimating future changes in the statistics of these rare events. These are usually projected using Regional Climate Model (RCM) scenario simulations combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to imperfections in the formulation of the physical parameterizations in the RCMs, the simulated present-day climate usually has biases relative to observations. Therefore, the RCM results are often bias-adjusted to match observations. This does, however, not guarantee that bias-adjusted projected results will match future reality better, since the bias may change in a changed climate. In the present work we evaluate different bias adjustment techniques in a changing climate. This is done in an inter-model cross-validation setup, in which each model simulation in turn plays the role of pseudo-reality, against which the remaining model simulations are bias adjusted and validated. The study uses hourly data from present-day and RCP8.5 late 21st century from 19 model simulations from the EURO-CORDEX ensemble at 0.11° resolution, from which fields of selected return levels are calculated for hourly and daily time scale. The bias adjustment techniques applied to the return levels are based on extreme value analysis and include analytical quantile-matching together with the simpler climate factor approach. Generally, return levels can be improved by bias adjustment, compared to obtaining them from raw scenarios. The performance of the different methods depends of the time scale considered. On hourly time scale, the climate factor approach performs better than the quantile-matching approaches. On daily time scale, the superior approach is to simply deduce future return levels from observations and the second best choice is using the quantile-mapping approaches. These results are found in all European sub-regions considered.

With a view to seasonal forecasting of extreme value statistics, we apply the method of Nonstationary extreme value statistics to determine the predictive power of large scale quantities. Regarding winter cold extremes over Europe, we find that the monthly mean daily minimum local temperature – which we call a native co-variate in the present context – has a much larger predictive power than the nonlocal monthly mean Arctic Oscillation index. Our results also prompt that the exploitation of both co-variates is not possible from 70 years-long data sets.

Severe precipitation events occur rarely and are often localized in space and of short duration, but are important for societal managing of infrastructure such as sewage systems, metros etc. Therefore, there is a demand for estimating expected future changes in the statistics of these rare events. These are usually projected using RCM scenario runs combined with extreme value analysis to obtain selected return levels of precipitation intensity. However, due to RCM imperfections, the modelled climate for the present-day usually has errors relative to observations. Therefore, the RCM results are ‘error corrected‘ to match observations more closely in order to increase reliability of results.In the present work we evaluate different error correction techniques and compare with non-corrected projections. This is done in an inter-model cross-validation setup, in which each model in turn plays the role of observations, against which the remaining error-corrected models are validated. The study uses hourly data (historical & RCP8.5 late 21st century) from 13 models covering the EURO-CORDEX ensemble at 0.11 degree resolution (about 12.5 km), from which fields of selected return levels are extracted for 1 h and 24 h duration. The error correction techniques applied to the return levels are based on extreme value analysis and include analytical quantile-quantile matching together with a simpler climate factor approach.The study identifies regions where the error correction techniques perform differently, and therefore contributes to guidelines on how and where to apply calibration techniques when projecting extreme return levels.

Predictability of extreme changes in the Arctic Ocean freshwater content and the associated release into the subpolar North Atlantic up to one decade ahead is investigated using a CMIP5-type global climate model. The perfect-model setup consists of a 500 year control run, from which selected 10 year long segments are predicted by initialized, perturbed ensemble predictions. Initial conditions for these are selected from the control run to represent large positive or negative decadal changes in the total freshwater content in the Arctic Ocean. Two different classes of ensemble predictions are performed, one initialized with the ‘observed’ ocean globally, and one initialized with the model climatology in the Arctic Ocean and with the observed ocean elsewhere. Analysis reveals that the former yields superior predictions 1 year ahead as regards both liquid freshwater content and sea ice volume in the Arctic Ocean. For prediction years two and above there is no overall gain in predictability from knowing the initial state in the Arctic Ocean and damped persistence predictions perform just as well as the ensemble predictions. Areas can be identified, mainly in the proper Canadian and Eurasian basins, where knowledge of the initial conditions gives a gain in predictability of liquid freshwater content beyond year two. Total freshwater export events from the Arctic Ocean into the subpolar North Atlantic have no predictability even 1 year ahead. This is a result of the sea ice component not being predictable and LFW being on the edge of being predictable for prediction time 1 year.

The performance of short-range operational forecasts of significant wave height (SWH) in the Baltic Sea is evaluated. Forecasts produced by a base configuration are intercompared with forecasts from two improved configurations: one with improved horizontal and spectral resolution and one with ensembles representing uncertainties in the physics of the forcing wind field and the initial conditions of this field. Both of the improved forecast classes represent an almost equal increase in computational costs. Therefore, the intercomparison addresses the question of whether more computer resources would be more favorably spent on enhancing the spatial and spectral resolution or, alternatively, on introducing ensembles. The intercomparison is based on comparisons with hourly observations of significant wave height from seven observation sites in the Baltic Sea during the 3-year period from 2015 to 2017. We conclude that for most wave measurement sites, the introduction of ensembles enhances the overall performance of the forecasts, whereas increasing the horizontal and spectral resolution does not. These sites represent offshore conditions, in that they are well exposed from all directions, are a large distance from the nearest coast and in deep water. Therefore, there is the a priori expectation that a detailed shoreline and bathymetry will not have any impact. Only at one site do we find that increasing the horizontal and spectral resolution significantly improves the forecasts. This site is situated in nearshore conditions, close to land and a nearby island, and is therefore shielded from many directions. Consequently, this study concludes that to improve wave forecasts in offshore areas, ensembles should be introduced. For near shore areas, in comparison, the study suggests that additional computational resources should be used to increase the resolution.

The performance of short-range operational forecasts of significant wave height in the Baltic Sea in three different configurations is evaluated. Forecasts produced by a base configuration are inter-compared with forecasts from two improved configurations: one with improved horizontal and spectral resolution and one with ensembles representing uncertainties in the physics of the forcing wind field and the initial conditions of this field. Both the improved forecast classes represent an almost equal increase in computational costs. The inter-comparison therefore addresses the question: would more computer resources most favorably be spent on enhancing the spatial and spectral resolution or, alternatively, on introducing ensembles? The inter-comparison is based on comparisons with hourly observations of significant wave height from seven observation sites in the Baltic Sea during the three-year period 2015–2017. We conclude that for most stations, the introduction of ensembles enhances the overall performance of the forecasts, whereas increasing the horizontal and spectral resolution does not. These stations represent offshore conditions, well exposed from all directions with a large distance to the nearest coast and with a large water depth. Therefore, the detailed shoreline and bathymetry is also a priori not expected to have any impact. Only for one station, we find that increasing the horizontal and spectral resolution significantly improved the forecasts. This station is situated in nearshore conditions, close to land, with a nearby island and therefore shielded from many directions. This study therefore concludes that to improve wave forecasts in offshore areas, ensembles should be introduced, while for nearshore areas better resolution may improve results.

Storm surges are a major concern for many coastal communities, and rising levels of surges is a key concern in relation to climate change. The sea level of a statistical 100-year or 1000-year storm surge event and similar statistical measures are used for spatial planning and emergency preparedness. These statistics are very sensitive to the assessments of past events, and to future sea level change.The probability of a major storm surge from the Baltic Sea hitting the Copenhagen metropolitan area is officially determined by the Danish Coastal Authority based on tide gauge records. We have a long history for tide gauge measurements, with 120 years of data available for the calculations. However, the oldest of these tide gauge stations was set up after a major storm surge in 1872, and no events of similar severity have occurred since.Including the evidence of the historic events from the 18th century changes the return period statistics, with a best estimate of a 100 year event changing from 1.5 meters (Sørensen et al. 2013) to 2.6 [2.2 – 2.8] meters (present study) in Køge just south of Copenhagen. Thus, with the tide gauge-based statistics, the storm surge on January 4 2017 was a 100 year event, but with the revised statistics using historic evidence, much larger events can be expected.Further, we assess the very large impact of sea level rise on the storm surge statistics. As an example, according to the official statistics of southern Copenhagen, the flooding of a present day 100 year event will statistically occur every 10 years with just 27 cm of mean sea level rise.

Global sea level rise is widely understood as a consequence of thermal expansion and the melting of glaciers and land-based ice caps. Because of the lack of representation of ice-sheet dynamics in present-day physically based climate models, semiempirical models have been applied as an alternative for projecting future sea levels. There are, however, potential pitfalls in this because of the trending nature of the time series. A statistical method called cointegration analysis that is capable of handling such peculiarities is applied to observed global sea level and land–ocean surface temperature. The authors find a relationship between sea level and temperature and find that temperature causally depends on the sea level, which can be understood as a consequence of the large heat capacity of the ocean. They further find that the warming episode in the 1940s is exceptional in the sense that sea level and warming deviate from the expected relationship. This suggests that this warming episode is mainly due to internal dynamics of the ocean rather than external radiative forcing. On the other hand, the present warming follows the expected relationship, suggesting that it is mainly due to radiative forcing. In a second step, the total radiative forcing is used as an explanatory variable, but it is unexpectedly found that the sea level does not depend on the forcing. The authors hypothesize that this is due to a long adjustment time scale of the ocean and show that the number of years of data needed to build statistical models that have the relationship expected from physics exceeds what is currently available by a factor of almost 10.

EC-Earth is a newly developed global climate system model. Its core components are the Integrated Forecast System (IFS) of the European Centre for Medium Range Weather Forecasts (ECMWF) as the atmo ...

This study investigates the possibility of reconstructing past global mean sea levels. Reconstruction methods rely on historical measurements from tide gauges combined with knowledge about the spatial covariance structure of the sea level field obtained from a shorter period with spatially well-resolved satellite measurements. A surrogate ensemble method is applied based on sea levels from a 500-yr climate model simulation. Tide gauges are simulated by selecting time series from grid points along continental coastlines and on ocean islands. Reconstructions of global mean sea levels can then be compared to the known target, and the ensemble method allows an estimation of the statistical properties originating from the stochastic nature of the reconstructions. Different reconstruction methods previously used in the literature are studied, including projection and optimal interpolation methods based on EOF analysis of the calibration period. This study also includes methods where these EOFs are augmented with a homogeneous pattern, with the purpose of better capturing a possible geographically homogeneous trend. These covariance-based methods are compared to a simple weighted mean method. It is concluded that the projection and optimal interpolation methods are very sensitive to the length of the calibration period. For realistic lengths of 10 and 20 yr, very large biases and spread in the reconstructed 1900–49 trends are found. Including a homogeneous pattern in the basis drastically improves the reconstructions of the trend and reduces the sensitivity to the length of the calibration period. The projection and optimal interpolation methods are now comparable to the weighted mean with biases less than 10% in the trend. However, the spread is still considerable. The amplitude of the year-to-year variability is in general strongly overestimated by all reconstruction methods. With regards to year-to-year variability, several methods outperform the simple mean. Finally, for the projection method, reconstruction errors are decomposed into contributions from the sparse coverage of tide gauges and the incomplete knowledge of the covariance structure of the sea level field. The study finds that the contributions of the different sources depend on the diagnostics of the reconstruction. It is noted that sea level is constrained by the approximate conservation of the total mass of the ocean. This poses challenges for the sea level reconstructions that are not present for other fields such as temperature.

Reconstruction of the earth’s surface temperature from proxy data is an important task because of the need to compare recent changes with past variability. However, the statistical properties and robustness of climate reconstruction methods are not well known, which has led to a heated discussion about the quality of published reconstructions. In this paper a systematic study of the properties of reconstruction methods is presented. The methods include both direct hemispheric-mean reconstructions and field reconstructions, including reconstructions based on canonical regression and regularized expectation maximization algorithms. The study will be based on temperature fields where the target of the reconstructions is known. In particular, the focus will be on how well the reconstructions reproduce low-frequency variability, biases, and trends. A climate simulation from an ocean–atmosphere general circulation model of the period a.d. 1500–1999, including both natural and anthropogenic forcings, is used. However, reconstructions include a large element of stochasticity, and to draw robust statistical interferences, reconstructions of a large ensemble of realistic temperature fields are needed. To this end a novel technique has been developed to generate surrogate fields with the same temporal and spatial characteristics as the original surface temperature field from the climate model. Pseudoproxies are generated by degrading a number of gridbox time series. The number of pseudoproxies and the relation between the pseudoproxies and the underlying temperature field are determined realistically from Mann et al. It is found that all reconstruction methods contain a large element of stochasticity, and it is not possible to compare the methods and draw conclusions from a single or a few realizations. This means that very different results can be obtained using the same reconstruction method on different surrogate fields. This might explain some of the recently published divergent results. Also found is that the amplitude of the low-frequency variability in general is underestimated. All methods systematically give large biases and underestimate both trends and the amplitude of the low-frequency variability. The underestimation is typically 20%–50%. The shape of the low-frequency variability, however, is well reconstructed in general. Some potential in validating the methods on independent data is found. However, to gain information about the reconstructions’ ability to capture the preindustrial level it is necessary to consider the average level in the validation period and not the year-to-year correlations. The influence on the reconstructions of the number of proxies, the type of noise used to generate the proxies, the strength of the variability, as well as the effect of detrending the data prior to the calibration is also reported.

The performance of a statistical downscaling model is usually evaluated for its ability to explain a large fraction of predictand variance. In this note, it is shown that although this fraction may be high, the longest time scales, including trends, may not be explained by the model. This implies that the model is nonstationary over the training period of the model, and it questions the basic stationarity assumption of statistical downscaling. This is exemplified by using a simple regression model for downscaling European precipitation and surface temperature where appropriate Monte Carlo–based field significance tests are developed, taking into account the intercorrelation between predictand series. Based on this test, it is concluded that care is needed in selecting predictors to avoid this form of nonstationarity. Even though this is illustrated for a simple regression-type statistical downscaling model, the main conclusions may also be valid for more complicated models.

Historical observations of multiyear ice, called “storis,” in the southwest Greenland waters exist from the period 1820–2000, obtained from ship logbooks and ice charts. It is argued that this ice originates in the Arctic Ocean and has traveled via the Fram Strait, southward along the Greenland coast in the East Greenland Current, and around the southern tip of Greenland. Therefore, it is hypothesized that these observations can be used as “proxies” for reconstructing the Fram Strait ice export on an annual basis. An index describing the storis extent is extracted from the observations and a linear statistical model formulated relating this index to the Fram Strait ice export. The model is calibrated using ice export values from a hindcast study with a coupled ocean–ice model over the period 1949–98. Subsequently, the model is used to reconstruct the Fram Strait annual ice export in the period 1820–2000. The model has significant skill, calculated on independent data. Based on this reconstruction...

We study the stability of the estimated statistical relation of global mean temperature and global mean sea-level with regard to data revisions. Using three different model specifications proposed in the literature, we compare coefficient estimates and forecasts using two different vintages of the annual time series. We find that two out of the three models, proposed in [1] and in [2], are very sensitive to the revisions. The magnitude of the estimated coefficient of influence as well as the implied long-term forecasts change drastically between the two data vintages considered. The model proposed in [3], on the other hand, reacts robustly to the revisions.

The surface of the world’s oceans has been warming since the beginning of industrialization. In addition to this, multidecadal sea surface temperature (SST) variations of internal origin exist. Evidence suggests that the North Atlantic Ocean exhibits the strongest multidecadal SST variations and that these variations are connected to the overturning circulation. This work investigates the extent to which these internal multidecadal variations have contributed to enhancing or diminishing the trend induced by the external radiative forcing, globally and in the North Atlantic. A model study is carried out wherein the analyses of a long control simulation with constant radiative forcing at preindustrial level and of an ensemble of simulations with historical forcing from 1850 until 2005 are combined. First, it is noted that global SST trends calculated from the different historical simulations are similar, while there is a large disagreement between the North Atlantic SST trends. Then the control simulation is analyzed, where a relationship between SST anomalies and anomalies in the Atlantic meridional overturning circulation (AMOC) for multidecadal and longer time scales is identified. This relationship enables the extraction of the AMOC-related SST variability from each individual member of the ensemble of historical simulations and then the calculation of the SST trends with the AMOC-related variability excluded. For the global SST trends this causes only a little difference while SST trends with AMOC-related variability excluded for the North Atlantic show closer agreement than with the AMOC-related variability included. From this it is concluded that AMOC variability has contributed significantly to North Atlantic SST trends since the mid nineteenth century.

For 40 precipitation series in Northwestern Europe covering the period 1900–1990, the question whether variability of winter (October to March) precipitation on all time scales longer than years can be explained by changes in circulation is investigated. This is done, for each time series, by applying a linear statistical method (multi-regression) linking the winter precipitation to the coefficients of the leading 5 principal components (PCs) of the winter mean mean sea level pressure. Having determined the coefficients, the corresponding hindcasted time series is obtained by applying the model. The interannual variability of winter precipitation is linked to circulation. This has been quantified by the multiple-correlation coefficient between the time series of observed and hindcasted values. Independence between these 2 time series is required for the validation to be meaningful. This is obtained by applying a cross-validation technique. The multiple-correlation coefficient is the largest in the Western Norway region. A comparison is also made between the performance of this standard model and a ‘reduced’ model based on the link between precipitation and the North Atlantic Oscillation (NAO) index only. This comparison shows that the performance is significantly larger using 5 PCs than using 1 PC (the NAO). In contrast, the lowest frequencies are not related to changes in the circulation. This shows up as a systematic positive trend in the difference between the observed and hindcasted precipitation for the majority of series. A Monte Carlo test reveals that this result is unlikely to have occurred by chance. This is interpreted as a change in the physics of the climate system, due to enhanced greenhouse forcing, to changes in sea surface temperatures in connection with some very low frequency mode, or to changes in land use enhancing the hydrological cycle.

In order to determine the response of the atmosphere to winter sea surface temperature (SST) anomalies in the North Atlantic area, we carried out ensemble runs of 20 years, forced with constant, perturbed, SST patterns using the climate version of the ARPEGE AGCM, at T42 resolution. A Monte Carlo technique was applied, in such a way that the control experiment, forced with observed climatological temperatures, and the four scenario experiments, forced with perturbed SSTs are equivalent to a length of 20 independent winters. Four anomalous winter North Atlantic sea surface temperature (SST) fields have been constructed by considering the observed SST variability in the main basins, namely the Labrador Sea and the Greenland Sea. Two patterns are of the `seesaw' type, while the two others have same the polarity in both basins. The patterns have been reinforced by a factor of 5–6 compared to presently observed multi-annual anomalies, in order to get SST anomalies which may have occurred during periods of the Little Ice Age. The differences between each of the four winter simulations with perturbed SSTs and the control run are analyzed in terms of tropospheric thickness, mean-sea-level pressure and storm activity. The `seesaw' type patterns give a weaker response in the tropospheric thickness fields than the two others. This is expected from simple considerations. In the mean circulation and synoptic activity, it appears that the Labrador Sea SST is important in determining the atmospheric response. This is probably due to enhanced temperature gradients east of New Foundland which enhances the storm activity.

The Late Maunder Minimum (LMM; 1675-1715) delineates a period with marked climate variability within the Little Ice Age in Europe. Gridded monthly mean surface pressure fields were reconstructed for this period for the eastern North Atlantic-European region (25°W-30°E and 35-70°N). These were based on continuous information drawn from proxy and instrumental data taken from several European data sites. The data include indexed temperature and rainfall values, sea ice conditions from northern Iceland and the Western Baltic. In addition, limited instrumental data, such as air temperature from central England (CET) and Paris, reduced mean sea level pressure (SLP) at Paris, and monthly mean wind direction in the Oresund (Denmark) are used. The reconstructions are based on a canonical correlation analysis (CCA), with the standardized station data as predictors and the SLP pressure fields as predictand. The CCA-based model was performed using data from the twentieth century. The period 1901-1960 was used to calibrate the statistical model, and the remaining 30 years (1961-1990) for the validation of the reconstructed monthly pressure fields. Assuming stationarity of the statistical relationships, the calibrated CCA model was then used to predict the monthly LMM SLP fields. The verification results illustrated that the regression equations developed for the majority of grid points contain good predictive skill. Nevertheless, there are seasonal and geographical limitations for which valid spatial SLP patterns can be reconstructed. Backward elimination techniques indicated that Paris station air pressure and temperature, CET, and the wind direction in the Oresund are the most important predictors, together sharing more than 65% of the total variance. The reconstructions are compared with additional data and subjectively reconstructed monthly pressure charts for the years 1675-1704. It is shown that there are differences between the two approaches. However, for extreme years the reconstructions are in good agreement.

This paper considers the analysis of long-term observational sea level data sets within the context of a possible change in storminess over North-West Europe. Sea level variations are studied as a proxy for storminess, i.e. in the synoptic frequency band. In that band, a proxy correspondence is assumed between sea levels and wind stress, although with some damping of the highest frequencies. Trends and variability over the past 100 yr are studied. However, a complicating factor is that many data sets include astronomical tidal influences (e.g. spring tide -neap tide cycle), which mask the sig- nal that is of most interest in this study. Sea level data sets from stations in the coastal zones of North- West Europe were collected, homogenised and made free of auto-correlation. To analyse these data sets, the 'quantile analysis method' is presented. This method involves an advanced analysis technique which, on the basis of a frequency analysis, determines the roughness/smoothness of succeeding decades in relation to the complete time period of a data set. Possible storm-related trends and varia- tions in this decadal quantity are defined by means of linear regression. Besides this main method, a time-shift variant of the quantile analysis method is applied in order to discuss the sensitivity of the obtained results. This sensitivity is also investigated with respect to the length of the time period in which a complete data set is split up. Although the analysis results show considerable natural variabil- ity on relatively short (decadal) time scales, no sign of a significant increase in storminess over North- West Europe is detected over the complete time period of the data sets. The results indicate a distinc- tion between stations in the German Bight and stations in the southern part of the North Sea. In the latter area, the natural variability is more moderate and there appears to be a tendency towards a weakening of the storm activity over the past 100 yr (not significant). Stations in the German Bight show a more enhanced natural variability on relatively short (decadal) time scales, with no indication of a weakening of the storm climate.

Northeast Atlantic winter storminess is analysed for the period 1875–1995 using a new dataset consisting of multi-daily mean sea level pressure observations from a selected set of stations in the northeast Atlantic. An analysis of storminess is presented, based on the high-pass filtered signal from these observations, from which selected percentiles are calculated for each winter. This method avoids potential inhomogeneity problems (artificial trends or jumps). Our finding is an increase, however not a dramatic one, during the past 2–3 decades in the northeasternmost part of the storm track, but the dominant features are inter-annual and decadal variations. Furthermore, the variations in storminess are found to be statistically linked to low-frequency circulation variations represented by the winter average of the MSL pressure field over the North Atlantic. It is argued that the existence of this link could have dynamical consequences for the response of the atmosphere to external forcing.

International audience; Sediment core ER07 from Sermilik Fjord by Helheim Glacier in Southeast Greenland was analysedfor alkenones to document sea surface temperature (SST) changes over the past 100 years. Thealkenone SST values, ranging from 8 to 12°C, contrasts with colder values (0-4°C) obtained fromrecent hydrographic surveys inside the fjord. We suggest that advection of allochtonous alkenonesproduced in the warm Irminger Current waters circulating on the shelf likely accounts for thisdifference. The temperature range of the alkenone-derived record is similar to in situ observationsof 8-11°C on the shelf just outside Sermilik Fjord, and its variability over the past 100 yearsresembles the constructed variability over the shelf using remote instrumental data. This suggeststhat oceanographic changes on the adjacent shelf are linked to regional changes of the IrmingerCurrent and the East Greenland Current. The subsurface water heat content has previously beensuggested as an important control on Greenland outlet glacier stability and underlined by an episodeof warm subsurface waters ~ 1940 concurrent with markedly increased calving and retreat ofHelheim Glacier. Our results therefore suggest that alkenone-derived SST time series from highsedimentation rate glacial fjords may provide a new approach for reconstruction of past changes ofshelf water properties and variability around Greenland.

Monthly means of sea level air pressure (SLP) and sea surface temperature (SSTJ fields are used as predictors to reconstruct an approximate climatology of wind and cyclonic activity, valid for 10 stations, in winter over the northern North Atlantic region throughout the 20th century. The reconstructed climatology is based on statistical relationships between these predictors and local observations at 10 synoptic stations during the period 1961 to 1987 The statistical relationships are identified by canonical correlation analysis combined with a cross validation technique. We apply double cross validation to independently verify the statistical model. An interpretati.on of the relationship between atmospheric circulation patterns and anomalies in the wind climatology and cyclonic activity is given. The historical climatology (1903 to 1987) for each observing station is obtained from the observed large-scale conditions by using the statistical relationships. It is found that within the present century both the monthly mean windiness and cyclonic activity show variations on the decadal time scale but no significant overall trends. This is confirmed for 2 of the stations for w h ~ c h directly observed pressure data were available since the beginning of the century.

Global sea levels are rising which is widely understood as a consequence of thermal expansion and melting of glaciers and land-based ice caps. Due to physically-based models being unable to simulate observed sea level trends, semi-empirical models have been applied as an alternative for projecting of future sea levels. There is in this, however, potential pitfalls due to the trending nature of the time series. We apply a statistical method called cointegration analysis to observed global sea level and surface air temperature, capable of handling such peculiarities. We find a relationship between sea level and temperature and find that temperature causally depends on the sea level, which can be understood as a consequence of the large heat capacity of the ocean. We further find that the warming episode in the 1940s is exceptional in the sense that sea level and warming deviates from the expected relationship. This suggests that this warming episode is mainly due to internal dynamics of the ocean rather than external radiative forcing. On the other hand, the present warming follows the expected relationship, suggesting that it is mainly due to radiative forcing. In a second step, we use the total radiative forcing as an explanatory variable, but unexpectedly find that the sea level does not depend on the forcing. We hypothesize that this is due to a long adjustment time scale of the ocean and show that the number of years of data needed to build statistical models that have the relationship expected from physics exceeds what is currently available by a factor of almost ten.

The EC-Earth consortium is a grouping of meteorologists and Earth-system scientists from 10 European countries, put together to face the challenges of climate and weather forecasting. The NWP system of the European Centre for Medium-Range Weather Forecasts (ECWMF) forms the basis of the EC-Earth Earth-system model. NWP models are designed to accurately capture short-term atmospheric fluctuations. They are used for forecasts at daily-to-seasonal time scales and include data assimilation capabilities. Climate models are designed to represent the global coupled ocean-atmosphere system. The atmospheric model of EC-Earth version 2, is based on ECMWF's Integrated Forecasting System (IFS), cycle 31R1, corresponding to the current seasonal forecast system of ECMWF. The EC-Earth consortium and ECMWF are collaborating on development of initialization procedures to improve long-term predictions. The EC-Earth model displays good performance from daily up to inter-annual time scales and for long-term mean climate.

Within the WASA project (von Storch et al. 1998; Bull Am Meterol Soc 79(5):741-760) an extensive data set containing station pressure values was used to calculate geostrophic winds (Alexandersson et al. 1998; Global Atmos Ocean Syst 6:97-120). Geostrophic winds were analysed in terms of percentiles to give a measure of long-term variations in synoptic-scale storminess. In this paper an update to 1998 is presented. In the Scandinavia, Finland and Baltic Sea area the most recent years, especially the cold and calm year 1996, seem to have brought an end to the stormy period centred on 1990. In the more westerly British Isles, North Sea and Norwegian Sea area, storminess is still at high levels compared with the less intense period between 1930 and 1980. The long-term increasing trend in NW Europe storminess that started in the 1960s seems to have been broken.

Ved at sammenligne geologiske undersøgelsermed moderne vejrdatamener vi nu at kunne skelne mellemnaturlige variationer i klimaet ogmenneskeskabte klimaændringer.Resultaterne peger på, at de sidste20-30 års opvarmning ikke kan forklaresaf naturlige processer.

Udgivelsesdato: 28. August 2009

Schmith, T.; Thejll, P., and Nielsen, J.W., 2016. Discussion of: Hansen, J. M., Aagaard, T. and Kuijpers, A., 2015. Sea-level forcing by synchronization of 56- and 74-year oscillations with the Moon's nodal tide on the northwest European shelf (eastern North Sea to central Baltic Sea). Journal of Coastal Research, 31(5), 1041–1056. We criticize important aspects of the analysis presented in Hansen, Aagaard, and Kuijpers (2015) (hereafter “HAK”) and thereby cast doubt on their conclusions. HAK claim that 18.6-year variations in sea level are supported by tidal theory, but this is not the case; therefore, the existence of such variations must be explicitly shown. We calculated the amplitude spectrum of the annual sea level by harmonic analysis and found no significant peaks at the periods claimed by HAK. Next, we used results given by HAK to reconstruct their decomposition and formed the residuals by subtracting the decomposition from the original data. We found that the variability near 18.6 years...

[1] An ensemble-hindcast approach is designed to simulate North Atlantic circulation changes during the period 1948–2005 using the Max Plank Institute Ocean Model forced by atmospheric reanalysis data. The experiment seeks to isolate oceanic changes related to the atmospheric forcing history by evaluating the role of initial conditions and masking internal model variability in the ensemble mean results. Characteristics of the complete North Atlantic–Arctic Mediterranean exchange system is described at key sections by time series of exchanges of volume, ice, heat, and liquid freshwater. Volume transports are divided into water masses by properties, and the constructed climatology of exchanges is shown to compare well with available observational estimates of individual branches. In response to the atmospheric forcing history, we find a modest but robust decline of the Atlantic meridional overturning circulation of 0.4 Sv/decade, in total 2–2.5 Sv since 1948. In contrast, overturning exchanges with the North Atlantic and Nordic Seas and with the Nordic Seas and Arctic Ocean show no sign of cessation. A marked increase in the freshwater storage of the Nordic Seas and in particular of the subpolar Atlantic accompany the model decline in Atlantic overturning. The characteristics of the modeled freshening bears similarities with observed patterns of change since the 1950s, but whereas the origin of observed freshening is uncertain, freshening in the model predominantly results from dynamic changes linked to prominent changes in atmospheric circulation. Moreover, documented increase in the drainage of major Arctic-rivers is found to have a negligible effect on the oceanic changes.