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A well-known exception to rising sea surface temperatures (SST) across the globe is the subpolar North Atlantic, where SST has been declining at a rate of 0.39 ( $$\pm$$ 0.23) K century−1 during the 1900–2017 period. This cold blob has been hypothesized to result from a slowdown of the Atlantic Meridional Overturning Circulation (AMOC). Here, observation-based evidence is used to suggest that local atmospheric forcing can also contribute to the century-long cooling trend. Specifically, a 100-year SST trend simulated by an idealized ocean model forced by historical atmospheric forcing over the cold blob region matches 92% ( $$\pm$$ 77%) of the observed cooling trend. The data-driven simulations suggest that 54% ( $$\pm$$ 77%) of the observed cooling trend is the direct result of increased heat loss from the ocean induced by the overlying atmosphere, while the remaining 38% is due to strengthened local convection. An analysis of surface wind eddy kinetic energy suggests that the atmosphere-induced cooling may be linked to a northward migration of the jet stream, which exposes the subpolar North Atlantic to intensified storminess.

A long simulation over the period 1901–2010 with an eddy-permitting ocean circulation model is used to study the variability of the upper layer circulation in the North Ionian Gyre (NIG) in the Eastern Mediterranean Sea (EMed). The model is driven by the atmospheric forcing from the twentieth century reanalysis data set ERA-20C, ensuring a consistent performance of the model over the entire simulation period. The main modes of variability known in the EMed, in particular the decadal reversals of the NIG upper layer circulation observed since the late 1980s are well reproduced. We find that the simulated NIG upper layer circulation prior to the observational period is characterized by long-lasting cyclonic phases with weak variability during years 1910–1940 and 1960–1985, while in the in-between period (1940–1960) quasi-decadal NIG circulation reversals occur with similar characteristics to those observed in the recent decades. Our simulation indicates that the NIG upper layer circulation is rather prone to the cyclonic mode with occasional kicks to the anticyclonic mode. The coherent variability of the NIG upper layer circulation mode and of the Adriatic Deep Water (AdDW) outflow implies that atmospheric forcing triggering strong AdDW formation is required to kick the NIG into an anticyclonic circulation 1–2 years later. A sensitivity experiment mimicking a cold winter event over the Adriatic Sea supports this hypothesis. Our simulation shows that it is the multi-decadal variability of the salinity in the Adriatic Sea that leads to periods where low salinity prevents strong AdDW formation events. This explains the absence of quasi-decadal NIG reversals during 1910–1940 and 1960–1985.

In August 2018, a remarkable polynya was observed off the north coast of Greenland, a perennial ice zone where thick sea ice cover persists. In order to investigate the formation process of this polynya, satellite observations, a coupled ice-ocean model, ocean profiling data, and atmosphere reanalysis data were applied. We found that the thinnest sea ice cover in August since 1978 (mean value of 1.1 m, compared to the average value of 2.8 m during 1978−2017) and the modest southerly wind caused by a positive North Atlantic Oscillation (mean value of 0.82, compared to the climatological value of −0.02) were responsible for the formation and maintenance of this polynya. The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind. Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.

Meteorological tsunamis (meteotsunamis) are defined as anomalous long-period (2 to 120 min) sea-level oscillations resulting from atmospheric forcing. In the current version of the Global Historical Tsunami Database covering almost 4000 years and including about 2500 tsunamis and tsunami-like events, meteotsunamis constitute a very small fraction of all events (4.1%). In the twenty-first century, when digital instruments for sea-level recording became widely available, identified meteotsunamis still only constitute 5.8% of all catalogued tsunami events. At the same time, there are many regions (Great Lakes, northeastern Gulf of Mexico, US East coast, southern Britain, Balearic Islands, Adriatic Sea, Yellow Sea, south-west coast of Japan, south-east coast of Brazil), where meteotsunamis dominate over all the other types of tsunamigenic events. Cataloguing of meteotsunami events, as reported in mass media, and described in scientific publications, faces the problems of their correct parameterization within the adopted format of the tsunami database. This format was developed in the late 1980s primarily for parameterization of seismogenic tsunamis, which at that time constituted more than 90% of the database’s content. As a result, most of the meteotsunamis included in the database lack some basic parameters, such as time of origin, location of source as well as run-up heights. The present paper addresses these issues and discusses the ways for their possible resolution. Several well-known cases of recent meteotsunamis are considered from the standpoint of their parameterization and hazard assessment.

We explore the response of wintertime Arctic sea ice growth to strong cyclones and to large-scale circulation patterns on the daily scale using Earth system model output in phase 5 of the Coupled Model Intercomparison Project (CMIP5). A combined metrics ranking method selects three CMIP5 models that are successful in reproducing the wintertime Arctic dipole (AD) pattern. A cyclone identification method is applied to select strong cyclones in two subregions in the North Atlantic to examine their different impacts on sea ice growth. The total change of sea ice growth rate (SGR) is split into those respectively driven by the dynamic and thermodynamic atmospheric forcing. Three models reproduce the downward longwave radiation anomalies that generally match thermodynamic SGR anomalies in response to both strong cyclones and large-scale circulation patterns. For large-scale circulation patterns, the negative AD outweighs the positive Arctic Oscillation in thermodynamically inhibiting SGR in both impact area and magnitude. Despite the disagreement on the spatial distribution, the three CMIP5 models agree on the weaker response of dynamic SGR than thermodynamic SGR. As the Arctic warms, the thinner sea ice results in more ice production and smaller spatial heterogeneity of thickness, dampening the SGR response to the dynamic forcing. The higher temperature increases the specific heat of sea ice, thus dampening the SGR response to the thermodynamic forcing. In this way, the atmospheric forcing is projected to contribute less to change daily SGR in the future climate.

The relative contributions of atmospheric fluctuations on 6 h−2 d, 2−8 d, and 8 d−1 month time scales to the changes in the air−sea fluxes, the SO circulation, and Antarctic sea ice are investigated. It was found that the imposed forcing variability on the three time scales creates a significant increase in wind power input, and hence an increase of about 50%, 97%, and 5% of eddy kinetic energy relative to the simulation driven by monthly forcing, respectively. Also, SO circulation and the strength of the upper cell of meridional overturning circulation become strengthened. These results indicate more dominant effects of atmospheric variability on the 2−8 d time scale on the SO circulation. Meanwhile, the 6 h−2 d (2−8 d) atmospheric variability causes an increase in the total sea-ice extent, area, and volume, by about 33%, 30%, and 19% (17%, 20%, and 25%), respectively, relative to those in the experiment forced by monthly atmospheric variables. Such significant sea-ice increases are caused by a cooler ocean surface and stronger sea-ice transports owing to the enhanced heat losses and air-ice stresses induced by the atmospheric variability at 6 h−2 d and 2−8 d, while the effects of the variability at 8 d−1 month are rather weak. The influences of atmospheric variability found here mainly result from wind fluctuations. Our findings in this study indicate the importance of properly resolving high-frequency atmospheric variability in modeling studies.

Significant summer land surface warming has been observed in the middle latitudes over East Asia, especially after the mid-1990s, which has evidently affected the East Asian weather and climate. Using multisource observations and reanalysis data during 1979–2013, this study explores the possible reasons for recent land surface warming over this region by considering atmospheric forcing and regional land–atmosphere interaction related to extratropical cyclones (ECs). Results show that there is a close relationship between land surface warming and weakened ECs over East Asia. Recent land surface warming was attributed to local atmospheric forcing and feedback of land–atmosphere interaction associated with weakened ECs. The abnormal large-scale circulation associated with anomalous ECs produced evident dynamic forcing on the land surface. Weakened ECs are usually accompanied by an abnormal high pressure system and anticyclonic circulation around Lake Baikal, which benefit the intensification of anomalous southerly wind in the rear of the anomalous anticyclone, leading to positive temperature advection and temperature increase over East Asia. Meanwhile, the anomalous adiabatic warming caused by abnormal descending motion associated with the anticyclonic anomaly also contributes to local warming. The feedback of local land–atmosphere interaction plays an important role in land surface warming. Weakened ECs increase both incident solar radiation and precipitation. The increased precipitation reduces the soil moisture and in turn weakens the surface evaporation and local cooling effect, resulting in land surface warming. Our findings are helpful for better understanding the mechanisms responsible for recent summer land surface warming over East Asia as well as its climatic effects.

The Southern Ocean (SO) surface wind stress is a major atmospheric forcing for driving the Antarctic Circumpolar Current and the global overturning circulation. Here the effects of wind fluctuations at different time scales on SO wind stress in 18 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are investigated. It is found that including wind fluctuations, especially on time scales associated with synoptic storms, in the stress calculation strongly enhances the mean strength, modulates the seasonal cycle, and significantly amplifies the trends of SO wind stress. In 11 out of the 18 CMIP5 models, the SO wind stress has strengthened significantly over the period of 1960–2005. Among them, the strengthening trend of SO wind stress in one CMIP5 model is due to the increase in the intensity of wind fluctuations, while in all the other 10 models the strengthening trend is due to the increasing strength of the mean westerly wind. These discrepancies in SO wind stress trend in CMIP5 models may explain some of the diverging behaviors in the model-simulated SO circulation. Our results suggest that to reduce the uncertainty in SO responses to wind stress changes in the coupled models, both the mean wind and wind fluctuations need to be better simulated.

Using a high-resolution precipitation dataset, the present study detected that the mountainous area of central China (MACA) is a hotspot of ENSO’s impact on the summer rainfall variability. Further analysis suggests that both ENSO and atmospheric forcing make contributions to the summer rainfall variability in MACA. The dominant rainfall-related SST mode features as a seasonal transition from an El Nino-like warming in the preceding winter to a La Nina-like cooling in the following autumn, and it explains about 29% of the total variance of the rainfall during 1951–2018. It indicates that ENSO with a rapid phase transition is responsible for inducing summer rainfall anomalies in MACA. Besides, an upper-level circumglobal wave mode in the Northern Hemisphere during summer also explains about 29% of the summer rainfall variance. Contributions of both the SST and the atmospheric modes have experienced interdecadal changes. The influence of the SST mode gradually increases and plays a dominant role in the recent decades, suggesting that ENSO with a rapid phase transition becomes more important for rainfall prediction in MACA.

Understanding water level (WL) fluctuations in river deltas is of importance for managing water resources and minimizing the impacts of floods and droughts. Here, we demonstrate the competing effects of atmospheric and oceanic forcing on multi-timescale variability and changes in the Pearl River Delta (PRD) WLs in southern China, using 52 years (1961–2012) of in-situ observations at 13 hydrological stations. PRD WL presents significant seasonal to decadal variations, with large amplitudes upstream related to strong variability of southern China rainfall, and with relatively small amplitudes at the coastal stations determined by sea level (SL) fluctuations of the northern South China Sea. We find that the strengths of atmospheric and oceanic forcing in PRD are not mutually independent, leading to a distinct contrast of WL–forcing relationships at upstream and coastal stations. In the transition zone, because of counteracts of atmospheric and oceanic forcing, no robust relationships are identified between WL and either of the forcing. We further show that in the drought season of the warm ENSO and PDO epochs, the effect of atmospheric (oceanic) forcing on PRD WL is largely enhanced (weakened), due to increased southern China rainfall and negative SL anomalies. Over the observation period, WL significantly decreased at upstream stations, by up to 28–42 mm/year for flood season, contrasting with the upward trends of

Summary: The internally generated variability in the climate system, which is unrelated to any external factors, can be conceptualized as “noise”. This noise is a constitutive element of high-dimensional nonlinear models of such systems. In a three-layer nested simulation, which is forced by climatological (periodic) atmospheric forcing and includes an (almost) global model, a West-Pacific model, and South China Sea (SCS) model, we demonstrate that such “noise” builds also ocean models. They generate variability by themselves without an external forcing. The “noise” generation intensifies with higher resolution, which favors macroturbulence. Keywords: “Noise”, Internal variability, South China Sea, Eddies, Model resolution

Stochastic variability of internal atmospheric modes, known as teleconnection patterns, drives large-scale patterns of low-frequency SST variability in the extratropics. To investigate how the decadal component of this stochastically driven variability in the South and North Pacific affects the tropical Pacific and contributes to the observed basinwide pattern of decadal variability, a suite of climate model experiments was conducted. In these experiments, the models are forced with constant surface heat flux anomalies associated with the decadal component of the dominant atmospheric modes, particularly the Pacific–South American (PSA) and North Pacific Oscillation (NPO) patterns. Both the PSA and NPO modes induce basinwide SST anomalies in the tropical Pacific and beyond that resemble the observed interdecadal Pacific oscillation. The subtropical SST anomalies forced by the PSA and NPO modes propagate to the equatorial Pacific mainly through the wind–evaporation–SST feedback. This atmospheric bridge is stronger from the South Pacific than the North Pacific due to the northward displacement of the intertropical convergence zone and the associated northward advection of momentum anomalies. The equatorial ocean dynamics is also more strongly influenced by atmospheric circulation changes induced by the PSA mode than the NPO mode. In the PSA experiment, persistent and zonally coherent wind stress curl anomalies over the South Pacific affect the zonal mean depth of the equatorial thermocline and weaken the equatorial SST anomalies resulting from the atmospheric bridge. This oceanic adjustment serves as a delayed negative feedback and may be important for setting the time scales of tropical Pacific decadal variability.

Sea surface temperature (SST) from four Nucleus for European Modelling of the Ocean (NEMO) model simulations is analyzed to study the bulk flux parameterization to compute SST over the Hudson Bay C...

Mechanisms driving the North Atlantic meridional overturning circulation (AMOC) variability at low frequency are of central interest for accurate climate predictions. Although the subpolar gyre region has been identified as a preferred place for generating climate time-scale signals, their southward propagation remains under consideration, complicating the interpretation of the observed time series provided by the Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) program. In this study, we aim at disentangling the respective contribution of the local atmospheric forcing from signals of remote origin for the subtropical low-frequency AMOC variability. We analyze for this a set of four ensembles of a regional (20°S–55°N), eddy-resolving (1/12°) North Atlantic oceanic configuration, where surface forcing and open boundary conditions are alternatively permuted from fully varying (realistic) to yearly repeating signals. Their analysis reveals the predominance of local, atmospherically forced signal at interannual time scales (2–10 years), whereas signals imposed by the boundaries are responsible for the decadal (10–30 years) part of the spectrum. Due to this marked time-scale separation, we show that, although the intergyre region exhibits peculiarities, most of the subtropical AMOC variability can be understood as a linear superposition of these two signals. Finally, we find that the decadal-scale, boundary-forced AMOC variability has both northern and southern origins, although the former dominates over the latter, including at the site of the RAPID array (26.5°N).

The Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), which uses unstructured grids, is set up for the North and Baltic Seas. With a resolution of ∼100 m in the narrow straits connecting the two basins, this model accurately resolves the inter-basin exchange. Validation against observations in the straits shows the model has good skill in simulating the transport and vertical profiles of temperature, salinity and currents. The timing and magnitude of the major inflow event in 2014–2015 is also realistically simulated. The analysis is focused on the two-layer exchange, its dependence on the atmospheric forcing, and dominant physical balances. The two-layer flows in the three connecting straits show different dependencies upon the net transport. The spatial variability of this dependence is also quite pronounced. The three-strait system developed specific dynamics, with time lags and differences between currents in the individual straits during inflow and outflow conditions. Analysis on the impact of resolution indicates that the performance of the model changes depending on whether the narrow parts of the straits are resolved with a resolution of 500 m or 100 m. With this ultra-fine resolution, gravity flows and variability of salinity in deep layers is generally more adequately simulated. This paper identifies the needs for more profound analysis of the coupled dynamics of Baltic and North Seas with a focus on the Danish straits.

The characteristics of storm surges obtained from sea level observations at four hydrometeorological stations in the North Caspian Sea for 2003–2017 are presented. The sea level that by 30 cm exceeds the monthly mean value at the analyzed point of the Caspian Sea was considered as a surge. In total, 370 surges were registered, 83% of them occurred during the cold season (September-April). The maximum surge height was 125 cm, the longest duration was 7 days. The most significant surges on Tyulenii Island were simulated with the operational hydrodynamic model of the sea level and currents of the Caspian Sea using atmospheric forcing from the COSMO model. The mean coefficient of correlation between the simulated and observed sea level is equal to 0.94.

Based on our previous study (Yu et al., Clim Dyn 49:1139–1156, 2017), this paper further investigates the interdecadal change in the relationship between the Kuroshio Extension (KE; 27°–37°N, 140°–158°E) and the East Asian summer monsoon (EASM) in the late 1980s. The summer KE sea surface temperature anomalies (SSTAs) show a significant positive relationship with the EASM over the period 1968–1987 (P1), but a significant negative connection with the EASM between 1991 and 2010 (P2). This interdecadal change in the KE–EASM relationship can be interpreted by considering the difference in the relationships of summer KE SSTAs with the East Asian subtropical westerly jet (EASWJ) and western North Pacific subtropical high (WNPSH) during the two periods. During P1, summertime KE SST warming is significantly related to the strengthened EASWJ and WNPSH, but it has close relationships with the weakened and northward-moving EASWJ and WNPSH during P2. These anomalous EASWJ and WNPSH associated with the summertime KE SST warming in P1 (P2) then favors increased (reduced) rainfall over the Yangtze River Valley that corresponds to a strong (weak) EASM, thereby leading to the significant positive (negative) KE–EASM relationship during this period. This change in the relationships of summer KE SSTAs with the EASWJ and WNPSH may be attributed to the increased KE SST variability associated with an enhanced Pacific Decadal Oscillation (PDO) in summer during P2, which is most probably induced by the stronger North Pacific Oscillation (NPO)-like atmospheric forcing, especially its southern pole (SP), in the preceding spring during this period. The spring NPO-like SP forces the KE SSTAs and PDO more directly during the following summer and can thus have been a better precursor for the following EASM than the full NPO-like dipole after the late 1980s.

The sensitivity of different atmospheric forcing on the simulation of Sea Surface Temperature (SST) in the Indian Ocean is examined using Regional Ocean Modeling System (ROMS). Model simulations using three different atmospheric forcings from the National Centers for Environmental Prediction (NCEP; 2.5 deg), National Centre for Medium Range Weather Forecasting (NCMRWF; 0.25 deg) and TropFlux (0.5 deg) are analyzed here. Model sensitivity to the atmospheric forcing is studied by analyzing the response of SST and mixed layer depth (MLD) using statistical methods. Results show that the response of NCMRWF and TropFlux forcing was almost similar in capturing the variability of SST in comparison with the corresponding observations. But NCEP was unable to capture SST variability, especially over the central part of the Arabian Sea (AS). It is shown that deeper MLD simulations by NCEP forcing due to the high magnitude of wind resulted in an unrealistic simulation of SST.

A data-assimilating ⅓° regional dynamical ocean model is evaluated on its ability to synthesize components of the Tropical Pacific Ocean Observing System. The four-dimensional variational data assimilation (4DVAR) method adjusts initial conditions and atmospheric forcing for overlapping 4-month model runs, or hindcasts, that are then combined to give an ocean state estimate for the period 2010–13. Consistency within uncertainty with satellite SSH and Argo profiles is achieved. Comparison to independent observations from Tropical Atmosphere Ocean (TAO) moorings shows that for time scales shorter than 100 days, the state estimate improves estimates of TAO temperature relative to an optimally interpolated Argo product. The improvement is greater at time scales shorter than 20 days, although unpredicted variability in the TAO temperatures implies that TAO observations provide significant information in that band. Larger discrepancies between the state estimate and independent observations from Spray gliders deployed near the Galápagos, Palau, and Solomon Islands are attributed to insufficient model resolution to capture the dynamics in strong current regions and near coasts. The sea surface height forecast skill of the model is assessed. Model forecasts using climatological forcing and boundary conditions are more skillful than climatology out to 50 days compared to persistence, which is a more skillful forecast than climatology out to approximately 20 days. Hindcasts using reanalysis products for atmospheric forcing and open boundary conditions are more skillful than climatology for approximately 120 days or longer, with the exact time scale depending on the accuracy of the state estimate used for initializing and on the reanalysis forcing. Estimating the model representational error is a goal of these experiments.

The Black Sea dynamics for two decades (1993–2012) is analyzed. The study is carried out in numerical simulation with the use of a circulation model and assimilation of satellite measurements of free surface elevations and sea-surface temperatures (SSTs). The Black Sea circulation model has a spatial resolution of 4.8 km and 35 vertical levels, which thicken toward the surface. Arrays of ERA-Interim data are used for simulating the atmospheric forcing. A set of 3D arrays of the Black Sea hydrophysical fields is calculated on a regular grid with a time resolution of 24 h. The results are analyzed and compared with available contact measurements of temperature and salinity. This comparison shows a quite good quality of the resulted hydrophysical fields of the Black Sea.

High resolution hybrid atmospheric water budget over the South Asian monsoon region is examined. The regional characteristics, variability, regional controlling factors and the interrelations of the atmospheric water budget components are investigated. The surface evapotranspiration was created using the High Resolution Land Data Assimilation System (HRLDAS) with the satellite-observed rainfall and vegetation fraction. HRLDAS evapotranspiration shows significant similarity with in situ observations and MODIS satellite-observed evapotranspiration. Result highlights the fundamental importance of evapotranspiration over northwest and southeast India on atmospheric water balance. The investigation shows that the surface net radiation controls the annual evapotranspiration over those regions, where the surface evapotranspiration is lower than 550 mm. The rainfall and evapotranspiration show a linear relation over the low-rainfall regions (

Observing System Simulation Experiments (OSSEs) have been conducted to evaluate the effect of Argo data assimilation on ocean reanalysis in the Pacific region. The “truth” is obtained from a 5-year model integration from 2003 to 2007 based on the MIT general circulation model with the truly varying atmospheric forcing. The “observations” are the projections of the truth onto the observational network including ocean station data, CTD, and various BTs and Argo, by adding white noise to simulate observational errors. The data assimilation method employed is a sequential three-dimensional variational (3D-Var) scheme within a multigrid framework. Results show the interannual variability of temperature, salinity, and current fields can be reconstructed fairly well. The spread of temperature anomalies in the tropical Pacific region is also able to be reflected accurately when Argo data is assimilated, which may provide a reliable initial field for the forecast of temperature and currents for the subsurface in the tropical Pacific region. The adjustment of salinity by using T-S relationship is vital in the tropical Pacific region. However, the adjustment of salinity is almost meaningless in the northwest Pacific if Argo data is included during the reanalysis.

From January through March 2017, a series of extreme precipitation events occurred in coastal Peru, causing severe floods with hundreds of human casualties and billions of dollars in economic losses. The extreme precipitation was a result of unusually strong recurrent patterns of atmospheric and oceanic conditions, including extremely warm coastal sea surface temperatures (SST) and weakened trade winds. These climatic features and their causal relationship with the Peruvian precipitation were examined. Diagnostic analysis and model experiments suggest that an atmospheric forcing in early 2017, which was moderately linked to the Trans-Niño Index (TNI), initiated the local SST warming along coastal Peru that later expanded to the equator. In January 2017, soil moisture was increased by an unusual expansion of Amazonian rainfall. By March, localized and robust SST warming provided positive feedback to the weakening of the trade winds, leading to increased onshore wind and a subsequent enhancement in rainfall. The analysis points to a tendency towards more frequent and stronger variations in the water vapor flux convergence along the equator, which is associated with the increased precipitation in coastal Peru.

This study analyzes the atmospheric forcing of upwelling occurrence along differently oriented coastlines of the southern Baltic Sea basin. The mean daily sea surface temperature (SST) data from the summer seasons (June&ndash, August) of the years 1982&ndash, 2017 made the basis for the detection of upwelling cases. For the atmospheric part of the analysis, monthly indices of four macroscale circulation patterns were used: North Atlantic Oscillation (NAO), Scandinavian (SCAND), East Atlantic (EA) and East Atlantic/Western Russia (EATL/WRUS). In order to identify the local airflows and wind conditions, zonal and meridional regional circulation indices were constructed and introduced to the analysis. Within the southern Baltic Sea basin, upwelling occurs most frequently along the zonally oriented southern coasts of Sweden, and least frequently along the southern (Polish) and eastern (Lithuanian-Latvian) coasts. Among the macroscale circulation patterns, the SCAND has the strongest impact on the horizontal flow of surface sea waters in the southern Baltic, which triggers upwelling. The summer NAO and EA appeared to have a weak effect on upwelling occurrence, and EATL/WRUS have the smallest impact. Local circulation indices allowed us to recognize the atmospheric control of upwelling frequency better than the indices of the macroscale patterns. Anomalies in upwelling frequency are their highest at the positive/negative phase of the zonal circulation, particularly along the southern and eastern coast of the southern Baltic Sea basin.

A performance study of seven regional ocean configurations based on NEMO has been carried out for the Adriatic Sea over a common period (1980–2012). Assessed models differ in resolution, model physics, atmospheric forcing (forced vs. coupled models) and river discharges imposed within the Adriatic Sea. Models have been evaluated on the long-term temperature and salinity measurements in all of the Adriatic sub-basins, in particular within dense water collectors (Jabuka Pit and South Adriatic Pit) and dense water formation sites (northern and southern Adriatic). Adriatic-wide salinity content is mostly linked to the proper introduction of the overall water budget, rather than to the local river forcing. Forced models mostly overestimate temperature and salinity values. On average, coupled models better reproduce the thermohaline properties and processes, in particular the Adriatic-Ionian Bimodal Oscillating System (BiOS) reversals and its decadal variability. Wintertime heat losses are playing major role in defining the Adriatic Deep Water (ADW) transport rates in coupled models, while in forced models preconditioning in salinity is the most important factor. Further on, increase of resolution of the atmospheric forcing results in more realistic ocean behaviour, including dense water formation (DWF) in the complex coastal northern Adriatic. However, all models have large temperature biases and lower deep stratification at the dense water collector sites, indicating overall underrepresentation of the Adriatic DWF. Consequently, mixed-layer depth in the southern Adriatic is overestimated, reaching the bottom during some years. Ocean model resolution and river forcing seem to play a second-order role in defining the overall Adriatic-Ionian thermohaline properties, while inclusion of aerosol trend only slightly modified the BiOS reproduction., One simulation was performed using HPC resources from GENCI-[TGCC/CINES/IDRIS], Grant 0227). AVISO+ product were downloaded from https://www.aviso.altimetry.fr. This work is a part of the Med-CORDEX initiative (www.medcordex.eu) and a contribution to the MISTRALS/HyMeX programme. The support from Croatian Science Foundation is acknowledged through projects ADIOS [HRZZ grant IP-06-2016-1955] and SCOOL [HRZZ grant IP-2014-09-5747].

Historical ocean reanalyses combine ocean general circulation models with data assimilation schemes that ingest rescued observations. They can be used as a tool to investigate long-term changes in the ocean climate. However, large uncertainties, due to the poorly developed atmospheric and oceanic observing networks in early periods, still remain. Thus, detailed studies to assess the uncertainty and its time dependency are required to quantify the feasibility of historical ocean reanalyses for climate change assessment. In this work, we estimate the ocean heat content variability from a set of ocean reanalyses that cover the period 1900–2010. The ocean reanalyses include realizations forced by two different atmospheric reanalyses (20CRv2 and ERA-20C), combined with different data assimilation strategies, in the attempt to evaluate the relative weight of the atmospheric forcing and observation uncertainties on the resulting ocean heat content estimates. Results suggest that even when observing networks are poor, the observations are able to shape the upper ocean heat content variability, in terms of long-term trends and reproduction of individual warming/cooling events related to volcanic eruptions. The assimilation of in-situ profiles has an effect even on the sea surface temperature variability and is able to constrain the top 700 m heat content since the 1950s with respect to the uncertainty borne by the atmospheric forcing. The vertical propagation of the upper ocean observational information is however slow (with typically decadal time scale). Consequently, the total column heat content is constrained by observations only in the latest two decades. We conclude that upper ocean heat content diagnostics from historical ocean reanalyses bear the climate change signature and may be considered for long-term studies when complemented by proper uncertainty estimation.

Running ensemble of reanalyses or forecasts has proved successful at improving their performances, despite the cost. Generating ensemble simulations requires generating perturbations within the models, and for the assimilated observations and subsidiary conditions. This paper proposes a statistical model to generate atmospheric forcing random perturbations in a flexible and cheap way, for all the variables required to calculate bulk formulae. The training data are a ten-year set of differences between ERA-INTERIM (from ECMWF) and MERRA (from NASA) atmospheric forcing fields, unbiased with a high-pass filter. The model is designed to generate global spatially-varying multi-variate and unbiased perturbations with consistent spatial structures. Based on linear regressions, the model allows for regression coefficients and residual standard deviations to vary with the time of the year. Once defined, the model does not rely on any other external data to generate the perturbations, and can hence be used on- or off-line in the goal of seeding the ensemble more appropriately. Once designed, the model has been validated by comparing three years of generated perturbations to three years of differences between the reanalyses out of the training period. Statistical tests show that the distributions of the sets comply reasonably well, except for precipitation and snow. The major issues come from regions with high kurtosis or where no clear patterns can be established. In terms of structure, the perturbation standard deviation of both sets show similar patterns for all variables.

Climate change and increased coastal urbanization are causing low-lying coastlines to become increasingly susceptible to the threat of extreme water levels and coastal flooding. Robust decision-making on adaptation in the coastal zone, based on reliable ocean-modelling tools, is therefore crucially contingent on accurate assessments of current and future storm surge hazards. This accuracy relies considerably on the quality of the wind forcing used in the ocean models. In this paper, we use a high-resolution, regional 3D ocean model (HBM) covering the North Sea and Baltic Sea to simulate extreme water levels during three extreme storm surge events with different dynamics and patterns, in order to assess their impacts along Denmark’s coastlines, which are of varying levels of complexity. We demonstrate that the model is able to reproduce the observed extreme high-water levels accurately, indicating that the system is well suited for producing simulations of present and future projections of extreme storm surges with high resulting impacts and damage potentials. Additionally, we quantify the level at which acknowledged deficiencies in the otherwise most suitable atmospheric forcing data set influence the results of the storm surge simulations. We found that reducing the temporal resolution of the forcing data – that is, replacing two out of every six time stamps with linearly interpolated values – is preferable to using the original forcing data set when recurring noise is present in these time stamps. As a result, for given storm surge events, and depending on the stage reached in the storm’s evolution, mean absolute errors can be reduced by 4.5 cm. This emphasizes the importance of considering such model fluctuations when coupling high-resolution atmosphere and ocean models.

Aerosol optical and radiative properties, measured with the ground based sun-sky radiometer (under AERONET), were explored at Kanpur in the central and Gandhi College (Ballia) in the Eastern Indo-Gangetic Plain (IGP) in Northern India during January to July 2019 (non-lockdown) and 2020 (lockdown). This year 2020 is marked an overall reduction of 15.79% and 3.39% in aerosol optical depth (AOD) than year 2019 at Kanpur and Gandhi College respectively. Lockdown 1 (23rd March to 14th April 2020) showed a reduction of 52.52% and 42.22% in AOD compared to pre-lockdown condition at Kanpur and Gandhi College respectively. Higher values of angstrom exponent measured at Eastern region (1.13 ± 0.17) than central region (0.97 ± 0.26) indicating the dominance of fine particles at Gandhi College. There was an overall reduction of 1.31% in single scattering albedo (SSA) at Kanpur while 4.34% reduction was observed at Gandhi College in year 2020 than 2019. The resultant atmospheric forcing and heating rate shows 11.86% reduction at Kanpur while 35.27% increase at Gandhi College during the lockdown period in 2020 compared to 2019. However, these values were found to decrease by 23.48% and 15.07% at Kanpur and Gandhi College respectively during the strict lockdown period compared to year 2019.

Two historical ocean reanalyses that cover the period from 1900 to 2010 are performed. One ocean reanalysis (CHOR_AS) assimilates both SST and vertical profile data with a 3D-var assimilation scheme, while the other one (CHOR_RL) assimilates vertical profile data, but is nudged to monthly HadISST reconstructed fields. The reanalyses are thus designed to investigate the impact of different strategies for constraining the SST in long-term assimilation experiments and the feasibility of assimilating subsurface observations in the same kind of experiments. The atmospheric forcing comes from the ensemble mean of the 20th Century Reanalysis version 2(20CRv2). Due to biases particularly large at high latitudes, corrections are applied to the atmospheric forcing, based on the comparison with ERA-Interim during the overlapped period. In general, these ocean reanalyses capture trends and variability of key ocean parameters. A warm bias of SST in CHOR_AS in the first half of the century leads to incorrect long-term trends. However, SST anomalies in both CHOR_AS and CHOR_RL are realistically captured. Heat contents at 0-300 m, 0-700 m and 0-2000 m have an increasing trend in both CHOR_RL and CHOR_AS, consistent with available datasets for the second half of the century. In order to reproduce realistic mass transport, mixed layer depth and eddy kinetic energy, it is necessary to assimilate vertical profile data, although this leads to some discontinuities in the reanalysis time series. Based on these findings, we suggest ideas for the requirements of future historical ocean reanalyses, such as related to the necessity of applying bias correction and minimize the effects of the observing network discontinuities. Meanwhile, the comparison between CHOR_RL and CHOR_AS shows that both direct assimilation and nudging scheme have their advantages and disadvantages. A combination of these two schemes is desirable in future work.

There is a large uncertainty of snow water equivalent (SWE) in reanalyses and the Global Land Data Assimilation System (GLDAS), but the primary reason for this uncertainty remains unclear. Here several reanalysis products and GLDAS with different land models are evaluated and the primary reason for their deficiencies are identified using two high-resolution SWE datasets, including the Snow Data Assimilation System product and a new dataset for SWE and snowfall for the conterminous United States (CONUS) that is based on PRISM precipitation and temperature data and constrained with thousands of point snow observations of snowfall and snow thickness. The reanalyses and GLDAS products substantially underestimate SWE in the CONUS compared to the high-resolution SWE data. This occurs irrespective of biases in atmospheric forcing information or differences in model resolution. Furthermore, reanalysis and GLDAS products that predict more snow ablation at near-freezing temperatures have larger underestimates of SWE. Since many of the products do not assimilate information about SWE and snow thickness, this indicates a problem with the implementation of land models and pinpoints the need to improve the treatment of snow ablation in these systems, especially at near-freezing temperatures.

Two techniques for perturbing surface initial conditions in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF) are presented and investigated in this paper. The first technique is the noncycling surface breeding (NCSB), which combines short-range surface forecasts driven by perturbed atmospheric forcing and the breeding method for generating the perturbations on surface initial conditions. The second technique, which is currently used in the ALADIN-LAEF operational version, applies an ensemble of surface data assimilations (ESDA) in which the observations are randomly perturbed. Both techniques are evaluated over a two-month period from late spring to summer. The results show that the evaluation is more favorable to ESDA. In general, the ensemble forecasts of the observed near-surface meteorological variables (screen-level variables) of ESDA are more skillful than NCSB, in particular for 2-m temperature they are statistically more consistent and reliable. A slightly better statistical reliability for 2-m relative humidity and 10-m wind has been found as well. This could be attributed to the introduction of surface data assimilation in ESDA, which provides more accurate surface initial conditions. Moreover, the observation perturbation in ESDA helps to better estimate the initial condition uncertainties. For the forecast of precipitation and the upper-air variables in the lower troposphere, both ESDA and NCSB perform very similarly, having neutral impact.

We quantify differences in ocean model simulations derived solely from atmospheric uncertainties and investigate how they relate to overall model errors as inferred from comparisons with data. For this purpose, we use a global configuration of the MITgcm to simulate 4 ocean solutions for 2000–2009 using 4 reanalysis products (JRA-25, MERRA, CFSR and ERA-Interim) as atmospheric forcing. The simulations are compared against observations and against each other for selected variables (temperature, sea-level, sea-ice, streamfunctions, meridional heat and freshwater transports). Forcing-induced differences are comparable in magnitude to model-observation misfits for most near-surface variables in the tropics and sub-tropics, but typically smaller at higher latitudes and polar regions. Forcing-derived differences are expectedly largest near the surface and mostly limited to the upper 1000 m but can also be seen as deep as 4000 m, especially in regions of deep water formation. Errors are not necessarily local in nature and can be advected to different basins. Results indicate that while forcing adjustments might suffice in optimization procedures of near-surface fields and at low-to-mid latitudes, other control parameters are likely needed elsewhere. Forcing-induced differences can be dominated by large spatial scales and specific time scales (e.g. annual), and thus appropriate error covariances in space and time need to be considered in optimization methodologies.

Uncertainty in continental shelf air-sea CO2 fluxes motivated us to investigate the impact of interannual and seasonal variabilities in atmospheric forcing on the capacity of three shelf regions along the U.S. eastern continental shelf to act as a sink or source of atmospheric CO2. Our study uses a coupled biogeochemical-circulation model to simulate scenarios of “present-day” and “future-perturbed” mesoscale forcing variability. Overall, the U.S. eastern continental shelf acts as a sink for atmospheric CO2. There is a clear gradient in air-sea CO2 flux along the shelf region, with estimates ranging from −0.6 Mt C yr−1 in the South Atlantic Bight (SAB) to −1.0 Mt C yr−1 in the Mid-Atlantic Bight (MAB) and −2.5 Mt C yr−1 in the Gulf of Maine (GOM). These fluxes are associated with considerable interannual variability, with the largest interannual signal exhibited in the Gulf of Maine. Seasonal variability in the fluxes is also evident, with autumn and winter being the strongest CO2 sink periods and summer months exhibiting some outgassing. In our future-perturbed scenario spatial differences tend to cancel each other out when the fluxes are integrated over the MAB and GOM, resulting in only minor differences between future-perturbed and present-day air-sea CO2 fluxes. This is not the case in the SAB where the position of the along-shelf gradient shifts northward and the SAB becomes a source of CO2 to the atmosphere (0.7 Mt C yr−1) primarily in response to surface warming. Our results highlight the importance of temperature in regulating air-sea CO2 flux variability.

The Community Land Model version 3.5 is driven by an observation-based meteorological dataset to simulate soil moisture over China for the period 1951–2008. A method for identifying the patterns of interannual variability that arise from slow (potentially predictable) and intraseasonal (unpredictable) variability is also applied; this allows identification of the sources of the predictability of seasonal soil moisture in China, during March–April–May (MAM), June–July–August (JJA), September–October–November (SON) and December–January–February (DJF). The potential predictability (slow-to-total) of the soil moisture above 1 m is high, with lowest value of 0.76 in JJA and highest value of 0.94 in DJF. The spatial distribution of the potential predictability comprises a northwest–southeast gradient, with a minimum center over East China and a maximum center over the northwest. The most important source of predictability is from the soil moisture persistence, which generally accounts for more than 50 % of the variability in soil moisture. The SSTs in the Indian Ocean, the North Atlantic and the eastern tropical Pacific Oceans are also identified as important sources of variability in the soil moisture, during MAM, JJA and SON/DJF, respectively. In addition, prolonged linear trends in each season are an important source. Using the slow principal component time series as predictands, a statistical scheme for the seasonal forecasting of soil moisture across China is developed. The prediction skills, in terms of the percentage of explained variance for the verification period (1992–2008), are 59, 51, 62 and 77 % during MAM–DJF, respectively. This is considerably higher than a normal grid prediction scheme.

Large-scale atmospheric forcing data can greatly impact the simulations of atmospheric process models including Large Eddy Simulations (LES), Cloud Resolving Models (CRMs) and Single-Column Models (SCMs), and impact the development of physical parameterizations in global climate models. This study describes the development of an ensemble variationally constrained objective analysis of atmospheric large-scale forcing data and its application to evaluate the cloud biases in the Community Atmospheric Model (CAM5). Sensitivities of the variational objective analysis to background data, error covariance matrix and constraint variables are described and used to quantify the uncertainties in the large-scale forcing data. Application of the ensemble forcing in the CAM5 SCM during March 2000 intensive operational period (IOP) at the Southern Great Plains (SGP) of the Atmospheric Radiation Measurement (ARM) program shows systematic biases in the model simulations that cannot be explained by the uncertainty of large-scale forcing data, which points to the deficiencies of physical parameterizations. The SCM is shown to overestimate high clouds and underestimate low clouds. These biases are found to also exist in the global simulation of CAM5 when it is compared with satellite data.

This study investigates the chemical composition of PM 10 aerosols at Varanasi, in the central Indo-Gangetic Plain (IGP) during April to July 2011, with emphasis on examining the contribution of elemental carbon (EC) to the estimates of direct aerosol radiative effect (DARE). PM 10 samples are analysed for carbonaceous aerosols (Organic Carbon, OC and EC) and water-soluble ionic species (WSIS: Cl − , SO 4 2− , NO 3 − , PO 4 2− NH 4 + , Na + , K + , Mg 2+ and Ca 2+ ) and several diagnostic ratios (OC/EC, K + /EC, etc) have been also used for studying the aerosol sources at Varanasi. PM 10 mass concentration varies between 53 and 310 μg m −3 (mean of 168 ± 73 μg m −3 ), which is much higher than the National and International air quality standards. The OC mass concentration varies from 6 μg m −3 to 24 μg m −3 (mean of 12 ± 5 μg m −3 ; 7% of PM 10 mass), whereas EC ranges between 1.0 and 14.3 μg m −3 (4.4 ± 3.9 μg m −3 ; ∼3% of PM 10 mass). The relative low OC/EC of 3.9 ± 2.0 and strong correlation (R 2 = 0.82) between them suggest the dominance of primary carbonaceous aerosols. The contribution of WSIS to PM 10 is found to be ∼12%, out of which ∼57% and 43% are anions and cations, respectively. The composite DARE estimates via SBDART model reveal significant radiative effect and atmospheric heating rates (0.9–2.3 K day −1 ). Although the EC contributes only ∼3% to the PM 10 mass, its contribution to the surface and atmospheric forcing is significantly high (37–63% and 54–77%, respectively), thus playing a major role in climate implications over Varanasi.

Climate Dynamics, 41 (3-4), ISSN:0930-7575, ISSN:1432-0894

Observational analyses reveal that wintertime variations in sea surface temperatures (SST) in the Kuroshio–Oyashio Extension (KOE) region of the North Pacific are associated with two distinct and robust patterns of atmospheric variability: 1) a pattern that peaks in amplitude approximately 2–3 weeks prior to large KOE SST anomalies and is consistent with atmospheric forcing of the SST field and 2) a very different pattern that lags SST anomalies in the KOE region by approximately a month. The latter pattern is dominated by low sea level pressure anomalies and turbulent heat fluxes directed into the atmosphere over warm SST anomalies and is interpreted as the transient atmospheric response to SST anomalies over the KOE region. The results contribute to a growing body of evidence that suggests variations in SSTs in the midlatitude oceans are capable of significantly influencing the large-scale atmospheric circulation, especially near western boundary currents.

Although atmospheric reanalyses are an extremely valuable tool to study the climate of polar regions, they suffer from large uncertainties in these data-poor areas. In this work, we examine how Antarctic sea ice biases in an ocean-sea ice model are related to these forcing uncertainties. Three experiments are conducted in which the NEMO-LIM model is driven by different atmospheric forcing sets. The minimum ice extent, the ice motion and the ice thickness are sensitive to the reanalysis chosen to drive the model, while the wintertime ice extent and inner pack concentrations are barely affected. The analysis of sea ice concentration budgets allows identifying the processes leading to differences between the experiments, and also indicates that large and similar errors compared to observations are present in all three cases. Our assessment of the influence of forcing inaccuracies on the simulated Antarctic sea ice allows disentangling two types of model biases: the ones that can be reduced thanks to better atmospheric forcings, and those that would require improvements of the physics of the ice or ocean model.

A 30-year meteorological station and reanalysis datasets revealed that the frequency and intensity of hot extremes have increased dramatically around Mongolia since the late 1990s. Many recent heat waves (HWs) were triggered by atmospheric forcing with a mid-tropospheric ridge pattern which appears frequently around Mongolia in 2000s. An observational data analysis for each HW event indicates that the rate of temperature increase at the beginning stage of HW is sensitive to the soil moisture condition. Therefore, it is suggested that the recent drier soil conditions played a role in enhancing the frequency and intensity of HWs and localization around Mongolia.

The mean and variability of the Atlantic meridional overturning circulation (AMOC), as represented in six ocean reanalysis products, are analyzed over the period 1960–2007. Particular focus is on multi-decadal trends and interannual variability at 26.5°N and 45°N. For four of the six reanalysis products, corresponding reference simulations obtained from the same models and forcing datasets but without the imposition of subsurface data constraints are included for comparison. An emphasis is placed on identifying general characteristics of the reanalysis representation of AMOC relative to their reference simulations without subsurface data constraints. The AMOC as simulated in these two sets are presented in the context of results from the Coordinated Ocean-ice Reference Experiments phase II (CORE-II) effort, wherein a common interannually varying atmospheric forcing data set was used to force a large and diverse set of global ocean-ice models. Relative to the reference simulations and CORE-II forced model simulations it is shown that (1) the reanalysis products tend to have greater AMOC mean strength and enhanced variance and (2) the reanalysis products are less consistent in their year-to-year AMOC changes. We also find that relative to the reference simulations (but not the CORE-II forced model simulations) the reanalysis products tend to have enhanced multi-decadal trends (from 1975–1995 to 1995–2007) in the mid to high latitudes of the northern hemisphere.

Interannual-to-decadal variability and trends of sea level in the South China Sea (SCS) are studied using altimetric data during 1993–2012 and reconstructed sea level data from 1950–2009. The interannual variability shows a strong seasonality. Surface wind anomalies associated with El Nino-Southern Oscillation explain the sea-level anomaly pattern in the interior SCS, while Rossby waves radiated from the eastern boundary dominate the sea-level variability in the eastern SCS. Decadal variability of sea level in the SCS follows that in the western tropical Pacific, with large variance found west of Luzon Island. Local atmospheric forcing makes a negative contribution to decadal variability in the central SCS, and Rossby waves radiated from the eastern boundary appear to be important. During 1993–2012, decadal sea level averaged in the SCS is significantly correlated with the Pacific Decadal Oscillation (PDO) (r = −0.96). The decadal variability associated with the PDO accounts for most part of sea-level trends in the SCS in the last two decades.

The mechanisms of coupled model bias in seasonal ENSO phase locking are investigated using versions 1.0 and 1.3 of the CSIRO–BOM ACCESS coupled model (hereafter, ACCESS1.0 and ACCESS1.3, respectively). The two ACCESS coupled models are mostly similar in construction except for some differences, the most notable of which are in the cloud and land surface schemes used in the models. ACCESS1.0 simulates a realistic seasonal phase locking, with the ENSO variability peaking in December as in observations. On the other hand, the simulated ENSO variability in ACCESS1.3 peaks in March, a bias shown to be shared by many other CMIP5 models. To explore the mechanisms of this model bias, we contrast the atmosphere–ocean feedbacks associated with ENSO in both ACCESS model simulations and also compare the key feedbacks with those in other CMIP5 models. We find evidence that the ENSO phase locking bias in ACCESS1.3 is primarily caused by incorrect simulations of the shortwave feedback and the thermocline feedback in this model. The bias in the shortwave feedback is brought about by unrealistic SST–cloud interactions leading to a positive cloud feedback bias that is largest around March, in contrast to the strongest negative cloud feedback found in ACCESS1.0 simulations and observations at that time. The positive cloud feedback bias in ACCESS1.3 is the result of a dominant role played by the low-level clouds in its modeled SST–cloud interactions in the tropical eastern Pacific. Two factors appear to contribute to the dominance of low-level clouds in ACCESS1.3: the occurrence of a stronger mean descending motion bias and, to a lesser extent, a larger mean SST cold bias during March–April in ACCESS1.3 than in ACCESS1.0. A similar association is found between the positive cloud feedback bias and the biases in spring-time mean descending motion and SST for a group of CMIP5 models that show a seasonal phase locking bias similar to ACCESS1.3. Significant differences are also found between the thermocline feedbacks simulated by ACCESS1.0 and ACCESS1.3 that appear to reinforce the seasonal ENSO phase locking bias in the latter model. We discuss a mechanism by which the thermocline feedback differences could arise from atmospheric forcing differences in the two models.

The main objective of this study is to investigate the variability of the thermohaline characteristics of the deep-water masses in the Aegean Sea and the possible impact of the regional atmospheric forcing variability by analyzing the available oceanographic and atmospheric datasets for the period of 1960–2012. During this period the variability of the deep water characteristics of the Aegean sub-basins is found to be very large as well as the diversity of the deep water characteristics among the sub-basins. The Central Aegean seems to play the key role in the Aegean deep water formation processes. Due to its small size, the Aegean Sea surface responds rapidly to the meteorological changes and/or the variability of the lateral fluxes and this variability propagates in the thermohaline characteristics of the deep water masses of the basin through deep water formation processes. There are many episodes characterized by a tight coupling of the atmosphere and the ocean during the examined period, with the Eastern Mediterranean Transient (EMT) being the most prominent case. We suggest that deep water formation is triggered mostly by the combination of preconditioning during early winter and/or previous winters together with the number of subsequent extreme events during present winter and not only by the total amount of the extreme heat loss winter days.

This study aims to estimate the influence of atmospheric circulation modes on future Baltic Sea level rise under the Representative Concentration Pathway 8.5 (RCP8.5) climate scenario for the period 2006–2100. For this estimation, the connection between the sea level variations in two selected representative locations—Stockholm and Warnemünde, and two atmospheric indices—the Baltic Sea and North Sea Oscillation (BANOS) index and the North Atlantic Oscillation (NAO) index is statistically analysed. Correlations of winter means between atmospheric indices, BANOS and NAO, and tide gauges are measured as 0.85 and 0.55 for Stockholm, and 0.55 and 0.17 for Warnemünde over the period 1900–2013. Assuming that the established connection remains unchanged, the influence of atmospheric circulation modes on future Baltic Sea level rise is estimated from the projections of atmospheric indices, which are constructed from the SLP outputs of climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the RCP8.5 scenario. The main conclusion is that the contribution of those atmospheric modes to the Baltic Sea level rise is likely to remain small over the 21st century. Additionally, corresponding trend estimations of model realizations indicate the large influence of the internal climatic variability of the CMIP5 models on those future trends. One of the most important findings of this study is that anthropogenic forcing does not play a key role in the evolution of these atmospheric indices.