globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2159
论文题名:
The rate of sea-level rise
作者: Anny Cazenave
刊名: Nature Climate Change
ISSN: 1758-1377X
EISSN: 1758-7497
出版年: 2014-03-23
卷: Volume:4, 页码:Pages:358;361 (2014)
语种: 英语
英文关键词: Physical oceanography ; Climate-change impacts
英文摘要:

Present-day sea-level rise is a major indicator of climate change1. Since the early 1990s, sea level rose at a mean rate of ~3.1 mm yr−1 (refs 2, 3). However, over the last decade a slowdown of this rate, of about 30%, has been recorded4, 5, 6, 7, 8. It coincides with a plateau in Earth’s mean surface temperature evolution, known as the recent pause in warming1, 9, 10, 11, 12. Here we present an analysis based on sea-level data from the altimetry record of the past ~20 years that separates interannual natural variability in sea level from the longer-term change probably related to anthropogenic global warming. The most prominent signature in the global mean sea level interannual variability is caused by El Niño–Southern Oscillation, through its impact on the global water cycle13, 14, 15, 16. We find that when correcting for interannual variability, the past decade’s slowdown of the global mean sea level disappears, leading to a similar rate of sea-level rise (of 3.3 ± 0.4 mm yr−1) during the first and second decade of the altimetry era. Our results confirm the need for quantifying and further removing from the climate records the short-term natural climate variability if one wants to extract the global warming signal10.

Precisely estimating present-day sea-level rise caused by anthropogenic global warming is a major issue that allows assessment of the process-based models developed for projecting future sea level1. Sea-level rise is indeed one of the most threatening consequences of ongoing global warming, in particular for low-lying coastal areas that are expected to become more vulnerable to flooding and land loss. As these areas often have dense populations, important infrastructures and high-value agricultural and bio-diverse land, significant impacts such as increasingly costly flooding or loss of freshwater supply are expected, posing a risk to stability and security17, 18. However, sea level also responds to natural climate variability, producing noise in the record that hampers detection of the global warming signal. Trends of the satellite altimetry-based global mean sea level (GMSL) are computed over two periods: the period 1994–2002 and the period 2003–2011 of the observed slowdown (Fig. 1a). GMSL time series from five prominent groups processing satellite altimetry data for the global ocean are considered (Methods). During recent years (2003–2011), the GMSL rate was significantly lower than during the 1990s (average of 2.4 mm yr−1 versus 3.5 mm yr−1). This is observed by all processing groups (Fig. 1a). The temporal evolution of the GMSL rate (computed over five-year-long moving windows, starting in 1994 and shifted by one year) was nearly constant during the 1990s, whereas the rate clearly decreased by ~30% after ~2003 (Fig. 2a). This decreasing GMSL rate coincides with the pause observed over the last decade in the rate of Earth’s global mean surface temperature increase9, 10, an observation exploited by climate sceptics to refute global warming and its attribution to a steadily rising rate of greenhouse gases in the atmosphere. It has been suggested that this so-called global warming hiatus11 results from El Niño–Southern Oscillation- (ENSO-) related natural variability of the climate system10 and is tied to La Niña-related cooling of the equatorial Pacific surface11, 12. In effect, following the major El Niño of 1997/1998, the past decade has favoured La Niña episodes (that is, ENSO cold phases, reported as sometimes more frequent and more intensive than the warm El Niño events, a sign of ENSO asymmetry19). The interannual (that is, detrended) GMSL record of the altimetry era seems to be closely related to ENSO, with positive/negative sea-level anomalies observed during El Niño/La Niña events2. Recent studies have shown that the short-term fluctuations in the altimetry-based GMSL are mainly due to variations in global land water storage (mostly in the tropics), with a tendency for land water deficit (and temporary increase of the GMSL) during El Niño events13, 14 and the opposite during La Niña15, 16. This directly results from rainfall excess over tropical oceans (mostly the Pacific Ocean) and rainfall deficit over land (mostly the tropics) during an El Niño20 event. The opposite situation prevails during La Niña. The succession of La Niña episodes during recent years has led to temporary negative anomalies of several millimetres in the GMSL (ref. 15), possibly causing the apparent reduction of the GMSL rate of the past decade. This reduction has motivated the present study. From seasonal to centennial time scales, the two main contributions to GMSL variability and change come from ocean thermal expansion and ocean mass. Owing to water mass conservation in the climate system, sources of global ocean mass variations are land ice masses, land water storage and atmospheric water vapour content. Studies have shown that ENSO-driven interannual variability in the global water cycle strongly impacts land water storage12, 13, 14, 15 and atmospheric water vapour21, hence ocean mass and GMSL.

Figure 1: GMSL trends during the 1994–2002 and 2003–2011 periods.
GMSL trends during the 1994-2002 and 2003-2011 periods.

a, GMSL trends computed over two time spans (January 1994–December 2002 and January 2003–December 2011) using satellite altimetry data from five processing groups (see Methods for data sources). The mean GMSL trend (average of the five data sets) is also shown. b, Same as a but after correcting the GMSL for the mass and thermosteric interannual variability (nominal case). Corrected means that the interannual variability due to the water cycle and thermal expansion are quantitatively removed from each original GMSL time series using data as described in the text. Black vertical bars represent the 0.4 mm yr−1 uncertainty (ref. 2).

Since the early 1990s, sea level has been routinely measured with quasi-global coverage and a few days/weeks revisit time by altimeter satellites: Topex/Poseidon (1992–2006), Jason-1 (2001–2013), Jason-2 (2008–), ERS-1 (1991–1996), ERS-2 (1995–2002), Envisat (2002–2011), Cryosat-2 (2010–) and SARAL/AltiKa (2013–). Altimetry-based GMSL time series are routinely produced by five processing groups: Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO; www.aviso.oceanobs.com/en/news/ocean-indicators/mean-sea-level), Colorado University (CU; www.sealevel.colorado.edu/), Commonwealth Scientific and Industrial Research Organization (CSIRO; www.cmar.csiro.au/sealevel/sl_data_cmar.html), Goddard Space Flight Center (GSFC; podaac.jpl.nasa.gov/Integrated_Multi-Mission_Ocean_AltimeterData) and National Oceanographic and Atmospheric Administration (NOAA; ibis.grdl.noaa.gov/SAT/SeaLevelRise/LSA_SLR_timeseries_global.pwhp). The GMSL time series from these five groups are based on Topex/Poseidon, Jason-1/2 missions. Recently, in the context of the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Level Project (www.esa-sealevel-chci.org), a new, improved product, combining the Topex/Poseidon, Jason-1/2 with the ERS-1/2 and Envisat missions, has been computed. At present, data up to December 2010 are available. Beyond that date, the CCI GMSL time series has been extended using the AVISO data. All products are considered here except the CSIRO one that uses older geophysical corrections for the Topex/Poseidon data. A small correction of −0.3 mm yr−1 is removed to each GMSL time series to account for the glacial isostatic adjustment effect (that is, the visco-elastic response of the solid Earth to the last deglaciation) on absolute sea level27. Owing to known errors in the Topex/Poseidon altimetric system in the early part of the mission, we ignore the year 1993 when computing the GMSL trends.

To estimate the mass component due to global land water storage change, we use the Interaction Soil Biosphere Atmosphere (ISBA)/Total Runoff Integrating Pathways (TRIP) global hydrological model developed at MétéoFrance22. The ISBA land surface scheme calculates time variations of surface energy and water budgets in three soil layers. The soil water content varies with surface infiltration, soil evaporation, plant transpiration and deep drainage. ISBA is coupled with the TRIP module that converts daily runoff simulated by ISBA into river discharge on a global river channel network of 1° resolution. In its most recent version, ISBA/TRIP uses, as meteorological forcing, data at 0.5° resolution from the ERA Interim reanalysis of the European Centre for Medium-Range Weather Forecast (www.ecmwf.int/products/data/d/finder/parametwer). Land water storage outputs from ISBA/TRIP are given at monthly intervals from January 1950 to December 2011 on a 1° grid (see ref. 22 for details). The atmospheric water vapour contribution has been estimated from the ERA Interim reanalysis. The land water storage and atmospheric water vapour contributions are further expressed in equivalent sea level (ESL) through weighting by the ratio of the total land and Earth areas to the ocean area and multiplied by −1. The land water plus atmospheric water vapour component was estimated over the January 1994–December 2011 time span.

Two thermal expansion data sets were considered: the V6.13 updated version of ocean temperature data down to 700 m, over January 1994–December 2006 (ref. 28) and Argo data down to 1,500 m over January 2007–December 2011 (ref. 29). As we focus on the interannual signal, we applied a high-pass filter (removing all signal >5 years) to the thermosteric time series. For the other data sets, a simple linear trend was removed (the ISBA/TRIP land water and atmospheric water vapour time series essentially display interannual variability; applying the high-pass filter or just removing a linear trend provides essentially the same results). The time series are estimated at monthly time steps. Annual and semi-annual signals are removed by fitting 12- and 6-month period sinusoids to each time series (using a climatology produces similar results). A four-month running mean smoothing is further applied to all time series. Errors in land surface modelling are generally mainly due to uncertainties in atmospheric forcing than in physicals parameterizations such as the representation of groundwater dynamics or not30. The global ISBA/TRIP simulation used here was extensively evaluated and the simulated global land water storage was found very close to the GRACE signal over their overlapping time span22. Errors of associated monthly mass component are estimated to 1.3–1.5 mm ESL (refs 22, 30). Errors on monthly water vapour component are <0.5 mm ESL. Errors on monthly thermosteric values are estimated to ~1.4 mm ESL (refs 28, 29).

In Figs 13, the mass component is based on ISBA/TRIP plus water vapour over the whole 1994–2011 time span (nominal case). Supplementary Figs 1, 2 and 3 use ISBA/TRIP outputs plus water vapour over 1994–2002 and GRACE data for 2003–2011 (hybrid case 1). In both cases, thermosteric data are from ref. 28 over 1994–2006 and Argo for 2007–2011.

  1. IPCC, Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
  2. Nerem, R. S., Chambers, D. P., Choe, C. & Mitchum, G. T. Estimating mean sea level change from the TOPEX and Jason altimeter missions. Mar. Geodesy 33, 435446 (2010).
  3. Church, J. A. & White, N. J. Sea-level rise from the late 19th to the early 21st century. Surveys Geophys. 32, 585602 (2011).
  4. Willis, J. K., Chambers, D. P. & Nerem, R. S. Assessing the globally averaged sea level budget on seasonal to interannual time scales. J. Geophys. Res. doi:10.1029/2007jc004517 (2008).
  5. Leuliette, E. W. & Miller, L. Closing the sea level rise budget with altimetry, Argo and GRACE. Geophys. Res. Lett. 36, L04608 (2009).
  6. Leuliette, E. W. & Willis, J. K. Balancing the sea level budget. Oceanography 24, 122129 (2011).
  7. Chen, J. L., Wilson, C. R. & Tapley, B. D. Contribution of ice sheet and mountain glacier melt to recent sea level rise. Nature Geosci. 6, 549552 (2013).
http://www.nature.com/nclimate/journal/v4/n5/full/nclimate2159.html
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/5198
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Anny Cazenave. The rate of sea-level rise[J]. Nature Climate Change,2014-03-23,Volume:4:Pages:358;361 (2014).
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