Svetlana Jevrejeva

How will sea level respond to changes in natural and anthropogenic forcings by 2100

Using an inverse statistical model we examine potential response in sea level to the changes in natural and anthropogenic forcings by 2100. With six IPCC radiative forcing scenarios we estimate sea level rise of 0.6-1.6 m, with confidence limits of 0.59 m and 1.8 m. Projected impacts of solar and volcanic radiative forcings account only for, at maximum, 5% of total sea level rise, with anthropogenic greenhouse gasses being the dominant forcing. As alternatives to the IPCC projections, even the most intense century of volcanic forcing from the past 1000 years would result in 10-15 cm potential reduction of sea level rise. Stratospheric injections of SO2 equivalent to a Pinatubo eruption every 4 years would effectively just delay sea level rise by 12-20 years. A 21st century with the lowest level of solar irradiance over the last 9300 years results in negligible difference to sea level rise

Anthropogenic forcing dominates sea level rise since 1850.

The rate of sea level rise and its causes are topics of active debate. Here we use a delayed response statistical model to attribute the past 1000 years of sea level variability to various natural (volcanic and solar radiative) and anthropogenic (greenhouse gases and aerosols) forcings. We show that until 1800 the main drivers of sea level change are volcanic and solar radiative forcings. For the past 200 years sea level rise is mostly associated with anthropogenic factors. Only 4 +/- 1.5 cm (25% of total sea level rise) during the 20th century is attributed to natural forcings, the remaining 14 +/- 1.5 cm are due to a rapid increase in CO2 and other greenhouse gases. Citation: Jevrejeva, S., A. Grinsted, and J. C. Moore (2009), Anthropogenic forcing dominates sea level rise since 1850, Geophys. Res. Lett., 36, L20706, doi: 10.1029/2009GL040216.

Influence of large-scale atmospheric circulation on European sea level: results based on the wavelet transform method

We examine relationships between the variability in the long-term time series of European sea level and the large-scale atmospheric circulation represented by the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) indices using the wavelet transform (WT). Results demonstrate that between 10% and 35% of the variance in winter mean sea level may be explained by the atmospheric circulation influence. However, the relationship between atmospheric circulation and sea level shows remarkable changes over time, especially between the earlier part of the twentieth century and the 1990s. Four dominant signals with periods 2.2, 3.5, 5.2 and 7.8 yr are detected and analysed by the WT using time series of sea level typically 150 yr long together with the NAO/AO indices. Cross-wavelet power and wavelet coherence confirm the linkages between the two parameters for selective time periods.

Singular spectrum analysis of Baltic Sea ice conditions and large‐scale atmospheric patterns since 1708

We examine the relationship between ice conditions in the Baltic Sea and the large-scale atmospheric circulation patterns that create them. Singular Spectrum Analysis (SSA) is used to extract long-term trends and quasi-regular oscillations from time series of winter air temperature, date of ice break-up, maximum annual ice extent in the Baltic Sea (BMI) since 1708 and seasonal North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) indices. The AO exhibits power at periods of 2.3 and 13.9 years as well as at 7.8 years, and in general the NAO can be viewed as a subset of the AO as regards the Baltic region. Time series of ice break-up date also reflects variations in winter AO power in the 13.9-year power band, but not in the NAO 7.8-year band. The BMI seems to act as a non-linear filter on the fairly weak climate oscillations, increasing the signal-to-noise ratio of the oscillations significantly.

New tools for analyzing time series relationships and trends

Geophysical studies are plagued by short and noisy time series. These time series are typically nonstationary contain various long-period quasi-periodic components, and have rather low signal-to-noise ratios and/or poor spatial sampling. Classic examples of these time series are tide gauge records, which are influenced by ocean and atmospheric circulation patterns, twentieth-century warming, and other long-term variability. Remarkable progress recently has been made in the statistical analysis of time series. Ghil et al. [2002] presented a general review of several advanced statistical methods with a solid theoretical foundation. This present article highlights several new approaches that are easy to use and that may be of general interest.

Oceanic and atmospheric transport of multiyear El Nino-Southern Oscillation (ENSO) signatures to the polar regions

[1] Using Monte-Carlo Singular Spectrum Analysis (MC- SSA) and Wavelet Transform (WT) we separate statistically significant components from time series and demonstrate significant co-variance and consistent phase differences between ice conditions and the Arctic Oscillation and Southern Oscillation indices (AO and SOI) at 2.2, 3.5, 5.7 and 13.9 year periods. The 2.2, 3.5 and 5.7 year signals detected in the Arctic are generated about three months earlier in the tropical Pacific Ocean. In contrast, we show that the 13.9 year signal propagates eastward from the western Pacificasequatorialcoupledwaves(ECW,0.13–0.15ms 1 ), and then as fast boundary waves (1–3 ms 1 ) along the western margins of the Americas, with a phase difference of about 1.8–2.1 years by the time they reach the Arctic. Our results provide evidence of dynamical connections between high latitude surface conditions, tropical ocean sea surface temperatures mediated by tropical wave propagation, the wintertime polar vortex and the AO. INDEX TERMS: 1620 Global Change: Climate dynamics (3309); 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); 0312 Atmospheric Composition andStructure: Air/seaconstituent fluxes (3339, 4504); 4504 Oceanography: Physical: Air/sea interactions (0312); 4522 Oceanography: Physical: El Nino. Citation: Jevrejeva, S., J. C. Moore, and A. Grinsted (2004), Oceanic and atmospheric transport