global sea level projections by 2100 - clivar · 2018. 9. 4. · sea level expansion glaciers...
TRANSCRIPT
Svetlana JevrejevaNational Oceanography Centre, Liverpool, UK
Global sea level projections by 2100
Outline
• Cause of sea level rise/Sea level budget
• Global sea level projections by 2100:
1. Process based approach
2. Probabilistic approach
3. Semi-empirical approach
• Uncertainties in sea level projections
• Short conclusion
Figure 13.27, AR5 IPCC ( 2013)
Sea Level Expansion Glaciers Land Water AntarcticaGreenland
Global sea level rise since 1700
Sea level budget
S- sea level
T- thermal expansion of the ocean
Mg- mass loss from glaciers
Gis – Greenland ice sheet
Ais- Antartcica ice sheet
Snc- None climatic component
Sea level budget since 1993
From A. Cazenave, http://www.psmsl.org/about_us/news/2013/workshop_2013/talks/02_PSMSL_Liverpool_28Oct2013_WEB.pdf
Figure 13.27, AR5 IPCC ( 2013)
Sea Level Expansion Glaciers Land Water AntarcticaGreenland
Global sea level rise by 2100
Processes contributing to sea level changes
Slangen et al., 2017
Process based approach
• AR5 IPCC, https://www.ipcc.ch/report/ar5/
• Climate forcing
• Climate /emission scenarios
• Coupled Model Intercomparison Project (CMIP5), World Climate Research
Programme (WCRP),
https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip5
• Modelling of individual components of sea level (excluding land water storage,
scenario independent)
• Challenges
Figure 8.18, AR5 IPCC
Radiative forcingRadiative forcing (W/m2) is “the rate of energy change per unit area of the globe as measured at the top of the atmosphere”
Where does the heat go?
➔
Net Heat Input to Earth System
(Levitus et al., GRL, 2004)
84% -- Saved by the oceans!
Amount of Heat Absorbed by Parts of Earth Climate System Over Past 40 Years
Scenarios: Representative Concentration Pathways (RCPs)
RCP8.5 (Representative Concentration Pathways )
GHG emissions continue to grow at current level
RCP2.6
Substantial reductions in emissions
3.7 °C
(2081-2100)
[2.6 4.8]
(2081-2100)
1 °C
[0.3 1.7]AR5 IPCC, 2013
Thermal expansion: modelling vs observations (0-700m)
Melet and Meyssignac, 2015
Thermosteric sea level (mm) referenced in 2005 for the 0–700-m layer
Thermosteric sea level (mm) referenced in 2005 for the full ocean depth
Melet and Meyssignac, 2015
Thermal expansion the full ocean depth: modelling vs observations
Thermal expansion: modelling vs observations
Slangen et al., 2017
Projections: Thermal expansion (CMIP5)
Global mean steric sea level change (zossga) over 21st century relative to 2006 for CMIP5 models for experiments (left) RCP 4.5, (middle) RCP 8.5 and (right) multi-model ensemble mean and 2σ
Modelling of individual components: Glaciers
Reviews of GeophysicsVolume 51, Issue 3, pages 484-522, 24 SEP 2013 DOI: 10.1002/rog.20015http://onlinelibrary.wiley.com/doi/10.1002/rog.20015/full#rog20015-fig-0010
Fig 4.12, AR5 IPCC
Glaciers
Glaciers
Slangen et al., 2016
Contribution from glaciers (mass balance)
Ice loss in Greenland
The rate of mass loss, in cm/yr water equivalent thickness, determined from monthly GRACE gravity field solutions, from Khan et al, 2010.
2003-2007 2003-2009
Straneo et al. 2012
Modelling contribution from Greenland ice sheet
Reviews of GeophysicsVolume 51, Issue 3, pages 484-522, 24 SEP 2013 DOI: 10.1002/rog.20015http://onlinelibrary.wiley.com/doi/10.1002/rog.20015/full#rog20015-fig-0004
Modelling of the 20th century contribution from Greenland
KK Kjeldsen et al. 2015
Slangen et al. 2017
Future Greenland ice sheet contribution
Slangen et al., 2016 [reproduced from Furst et al. (2015)].
Reviews of GeophysicsVolume 51, Issue 3, pages 484-522, 24 SEP 2013 DOI: 10.1002/rog.20015http://onlinelibrary.wiley.com/doi/10.1002/rog.20015/full#rog20015-fig-0006
Contribution from Antarctica ice sheet
Large Ensemble model analyses of future Antarctic contributions to GMSL
R M DeConto et al. Nature 531, 591–597 (2016) doi:10.1038/nature17145
Historical and projected terrestrial water contributions to sea level rise
Land water storage
Slangen et al., 2017 (based on Wada et al., 2012)
Slangen et al., 2017
Modelled total sea level changes since 1900s
Global sea level projections by 2100 in AR5 IPCC
AR5 IPCC, 2013
Median values and likely ranges for projections of global mean sea level (GMSL) rise and its contributions in metres in 2081–2100 relative to 1986–2005 for the four RCP scenarios and SRES A1B, GMSL rise in 2046–2065 and 2100, and rates of GMSL rise in mm/yr in 2081–2100 (AR5 IPCC, 2013).
Uncertainties in global sea level projections
Credits: Finnish Meteorological Institute
Likely range (66% probability)
AR5 IPCC, 2013
1879
1928
1890
1953
Photo from Environment Agency, UK
Photo from Environment Agency, UK
Probabilistic approach
AR5 IPCC, 2013
Likely range (66% probability)
Jevrejeva et al, 2014
Probabilistic approach in global sea level projections
Probabilistic approach in global sea level projections
Expansion
Glacier
Greenland
Antarctica
0 0.2 0.4 0.6 0.8 1
Landwater
Projected sea level contribution by 2100 (m)
Likely range (IPCC)
Our study
Jevrejeva et al, 2014
Semi-empirical approach
Reviews of GeophysicsVolume 51, Issue 3, pages 484-522, 24 SEP 2013 DOI: 10.1002/rog.20015http://onlinelibrary.wiley.com/doi/10.1002/rog.20015/full#rog20015-fig-0001
2007 2013
Approach:• Smooth GSL record (1880-2000)
• Calculate dH/dt
• Linear regression against observed T.
• Use projected temperatures to project GSL
Semi-empirical model by Rafmstorf, 2007
Model including a response time (Grinsted et al, 2010)
S=f(T)
Parameters:
(τ, a, b, S0)
baTSeq += (eq. 1)
Inverse problem
• We know T(Temperature, 2000 yrs)
• We know S(Tide gauges, 300 yrs)
• We do not know the model parameters that allow us to calculate S from T: a, b, τ, S0
model:
S=f(T)
Tem
pe
ratu
re
Likelihood of the model
How well does S match observations taking into account the uncertainties in observed sea level
C is the uncertainty covariance matrix. This takes into account that the observations are not independent
PDFs for model parameters
Sea level projections (Using A1B temperatures)
AR4 IPCC A1B
AR4 IPCC A1B
Grinsted et al., 2010
Global mean sea level rise (metres) in 2081–2100 relative to 1986–2005 by semi-empirical models (bars) and process basedmodels (grey colour) for (a) RCP2.6, (b)RCP4.5, (c) RCP6.0 and (d) RCP8.5.
Figure 13.12 , AR5 IPCC
Grey colour (process based) is 17-83% Blue and red (SE) are 5-95%
NOTE:
Projections from process based and semi-empirical approaches
Limitations/Uncertainties
1. The largest uncertainties are associated with contribution from Greenland and Antarctica ice sheets: • Ocean-ice sheet interaction• Ice dynamics• Limited number of models/observations
2. Glaciers: • No ice dynamics, • Limited number of models • Limited number of observations to calibrate models
3. Thermal expansion• Deep ocean • Heat update by the ocean• Lack of observations below 2000m• Lack of observations prior 1955
Svetlana JevrejevaNational Oceanography Centre, Liverpool, UK
Regional and local sea level projections by 2100
Outline
1. Background (global –regional- local)
2. Physical mechanisms for the regional changes:
• Ocean dynamics
• Gravitational forcing (fingerprints)
• Vertical land movement
3. Uncertainties in regional and local sea level projections
4. Conclusion
1993-2008
Cazenave and Llovel, 2010AR5 IPCC, 2013
Global Regional Local
Processes contributing to sea level changes
Slangen et al., 2017
Fingerprints
Global Regional Local
Tamisiea and Mitrovica, 2011
Sea level in each grid point
(SAL) - the impact of self-attraction and loading of the ocean upon itself; due to the long term alteration of ocean density changes;(STR)- globally averaged steric sea-level rise;(DSL)- dynamic sea-level change; (GLA)- glaciers ;(GRE)- Greenland ice sheet; (ANT)- Antarctic ice sheet;(LAN)- land-water storage;(GIA)- Glacial Isostatic Adjustment;(TECT)- tectonics; (NCLIM)- non-climatic land-motion
Jackson and Jevrejeva, 2016
Normalised pattern due to gravitational and Earth rotational effects
Jackson and Jevrejeva, 2016
a) Glaciers (Bamber & Riva, 2010), b) Greenland (Bamber & Riva,
2010), c) Antarctica (Bamber & Riva, 2010) d) Land-water (Wada et al. 2012).
Future contribution from cryosphere
Jevrejeva et al., 2016
2040 2080 2100
Ocean component (CMIP5)
Jevrejeva et al., 2016
2040 2080 2100
ModelNumber of realisations for RCP8.5
Reference
bcc-csm1-1 1 Wu et al. (2010)
bcc-csm1-1-m 1 Wu et al. (2010)
CanESM2 5 Arora et al. (2011)
CMCC-CESM 1
CMCC-CM 1 Scoccimarro et al. (2011)
CMCC-CMS 1
CNRM-CM5 5 Voldoire et al. (2013)
ACCESS1-0 1 BOM (2010)
ACCESS1-3 1 BOM (2010)
CSIRO-MK3-6-0 10 Rotstayn et al. (2010)
EC-EARTH 12 Hazeleger et al. (2010)
inmcm4 1 Volodin et al. (2010)
IPSL-CM5A-LR 4 Dufresne et al. (2013)
IPSL-CM5A-MR 1 Dufresne et al. (2013)
IPSL-CM5B-LR 1 Dufresne et al. (2013)
FGOALS-g2 1 Yongqiang et al. (2004)
MIROC5* 3 Watanabe et al. (2010)
MIROC-ESM 1 Watanabe et al. (2011)
MIROC-ESM-CHEM 1 Watanabe et al. (2011)
HadGEM2-CC 3 Martin et al. (2011)
HadGEM2-ES 4 Collins et al. (2011)
MPI-ESM-LR 3 Raddatz et al. (2007)
MPI-ESM-MR 1 Raddatz et al. (2007)
MRI-CGCM3 1 Yukimoto et al. (2001)
GISS-E2-R* 3 Schmidt et al. (2006)
CCSM4 6 Gent et al. (2011)
NorESM1-M 1 Iversen et al. (2013)
NorESM1-ME 1 Iversen et al. (2013)
GFDL-ESM2G 1 Donner et al. (2011)
GFDL-ESM2M 1 Donner et al. (2011)
CESM1-BGC 1 Vertenstein et al. (2012)
CESM1-CAM5 2 Vertenstein et al. (2012)
CESM1-WACCM 3 Vertenstein et al. (2012)
Total models 33
Total realisations 83
AOGSMs from CMIP5
Median
95%
Jevrejeva et al, 2016
Global Regional Local
Regional sea level projections with RCP8.5 by 2100
95%
Median
Ratio of projected local (1°grid cells close to coastline) median and upper limit (50%/95%) sea level rise to global median sea level rise
2040
2080
2100
Jevrejeva et al, 2016
Sea level rise with RCP8.5 along the coastlines
Coastal sea level: RCP4.5
Carson et al., 2016
Grinsted et al, 2015
5% 50% 95% 99%
Belfast0.27 0.64 1.57 2.22
Newlyn0.45 0.82 1.81 2.49
Cardiff0.40 0.77 1.73 2.40
Edinburgh0.26 0.64 1.56 2.20
Liverpool0.35 0.71 1.66 2.31
Aberdeen0.27 0.66 1.58 2.21
London0.43 0.81 1.76 2.43
RCP8.5
Sea level projections by 2100 for the UK locations
Sea level projections for Individual locations
95%
50%
Guangzhou Miami Maldives
Jevrejeva et al, 2016
Sea level rise for individual cities by 2100 (RCP8.5)
Uncertainties at individual locations (New York)
Earth's FutureVolume 2, Issue 8, pages 383-406, 21 AUG 2014 DOI: 10.1002/2014EF000239http://onlinelibrary.wiley.com/doi/10.1002/2014EF000239/full#eft237-fig-0004
Kopp et al, 2014
Thermal expansion
Greenland ice sheet
Antarctica ice sheet
Earth's FutureVolume 2, Issue 8, pages 383-406, 21 AUG 2014 DOI: 10.1002/2014EF000239http://onlinelibrary.wiley.com/doi/10.1002/2014EF000239/full#eft237-fig-0004
global
New York
Kopp et al., 2014
Kopp et al., 2014
Jevrejeva et al., 2016
Jevrejeva et al., 2016
Uncertainties in sea level projections
ICE 5G- ICE 1
ICE 5G- ICE 4G
ICE 5G- KL05
ICE 5G- ICE 3G
Jevrejeva et al, 2014
Uncertainties due to GIA (Glacial Isostatic Adjustment) corrections
Credits to Deltares
King et al, 2012
Top ten cities of each scenario listed in each panel are coloured whilst all other cities are plotted in grey
Sea level projections (median, 50%) without local vertical land movement
Sea level projections (median, 50%) with local vertical land movement
Probabilistic projections of extreme sea levels (sea level rise +waves+storm surges)
Vousdoukas et al., 2018
Return period of the present day 100-year ESL under RCP4.5 and RCP8.5 in 2050 and 2100
Probabilistic projections of extreme sea levels (sea level rise + waves + storm surges)
Vousdoukas et al., 2018
1. Sea level community is making a substantial progress in understanding of global and regional sea level rise and variability
1. The key uncertainties (global/regional/local) area) emission scenariosb) contribution from ice sheets
3. The largest uncertainties in regional and local sea level projections associated with ocean dynamics and the vertical land movement
4. The main challenges for coast projections:
• AOGSMs do not have resolution, physical mechanisms, topography to resolve coastal processes on the shelf
• Semi-enclosed seas (e.g. Mediterranean) are not resolved in AOGSMs• Decadal variability in ocean dynamics• Local vertical land movement
Conclusion (regional and local sea level projections)
5. Probabilistic sea level projections in coastal areas is a valuable solution for the risk assessment and decision making about the adaptation. However, probabilistic approach (or conventional approach) do not consider interaction between the components.
6. Challenges: interaction between physical mechanisms (e.g. river runoff with waves, tides, rainfall, storm surges, sediment transport, erosion) is available for specific events or short term simulations. Combined effect on the coast is not quantified.
7. Impact of sea level rise in the coastal areas is already seen and every 10 cm by 2100 could result in additional global annual flood damages of US$ 1.5 trillion per year (0.25% of global GDP) without adaptation. For many countries (e.g. China, EU countries) >1% GDP for every 10 cm sea level rise.
8. The large part of the coast is not covered with observations (tide gauges, waves, vertical land movement), we urgently need a novel instruments.
The main challenges for coast projections (continue):
Sea flood damage costs with the sea level rise by 2100
Global sea floods cost,Million US$ per year
Global sea floods cost, % of GDP (global)
Sea flood cost for China, % of GDP (China)
China, flood cost in 2100US$ 3.4 trillion per year (5.8 % GDP) with warming of 1.5 degree (0. 5 m sea level rise) US$ 4.6 trillion per year (7.8% GDP) with RCP8.5 (0. 8 m sea level rise)US$ 8.5 trillion per year (14 % GDP) with RCP8.5J14 (1.8 m sea level rise)
Jevrejeva et al., 2018
1.5 degree
RCP8.5
RCP8.5J14
Sea flood damage costs with the sea level rise by 2100
Global sea flood cost,Million US$ per year
Global sea flood cost, % of GDP (global)
UK sea flood cost,% of UK GDP
UK flood cost in 2100US$ 241 billion per year (2.5 % UK GDP) with warming of 1.5 degree (0. 5 m sea level rise) US$ 619 billion per year (6.5% UK GDP) with RCP8.5 (0. 8 m sea level rise)
US$ 1.1. trillion per year (11.1 % UK GDP) with RCP8.5J14 (1.8 m sea level rise) Jevrejeva et al., 2018