Prof. Stephen Briggs

European Space Agency

ISRSE, Tshwane

10 May 2017

Satelite observations contributing to

global change science and policy

Credit: Victor & Kennel, Nature Climate Change, 2014.

The importance of observations………

First image of the Earth from space

1990 1996 2000 2004 2008 2012

50

0

-50

-100

-150

-200

-250

-300

-350

Ice

mas

s lo

ss (

Gt/

yr)

-400

Previous surveys: Antarctica

imbie

Gravimetry

Radar Altimetry

Interferometry

Laser altimetry

1mm

50

0

-50

-100

-150

-200

-250

-300

-350

1990 1996 2000 2004 2008 2012

Ice

mas

s lo

ss (

Gt/

yr)

-400

Previous surveys: Greenland

imbie

Gravimetry

Radar Altimetry

Interferometry

Laser altimetry

1mm

ESA-NASA IMBIE Initiative

1987 1992 1997 2002 2007 2012

0

Combined

Greenland

Antarctica

Increased ice sheet mass losses

10

8

6

4

2

12

0.27 mm per yr

0.95 mm per year

imbie

Sea

leve

l co

ntr

ibu

tio

n (

mm

)

CryoSat: Yearly Ice Loss in Greenland

Courtesy: Helm et al. (2014)

Digital Elevation Model

2003-2008 2011-2012

IMBIE CryoSat

-189±20 km3/yr

-352±29 km3/yr

• Unique (first ever) combination of S1A stripmap and TerraSAR-X SAR data

provides first map of Austfonna ice speed in 2014

• Data show that glacier at Cap Mohn has experienced a rapid acceleration

Ice Speed (kilometres per year)

0 4

1995 (ERS) 2008 (ALOS) 2014 (TSX-S1)

Credit: N. Gourmelen, University of Edinburgh

Sentinel-1A: Svalbard Ice Cap

Slide 10

Sea Ice Thickness from SMOS

Copyright:University of Hamburg

Slide 11

SMOS & Cryosat: Expanding HorizonsCopyright: AWI

Arctic sea ice 2016-2017

Arctic Sea Ice Volume 1979-2017

Polar shipping summer 2014

Glacierarea

Inventory

Elevation change

dh/dttrendsfrom

altimetry

mean changes fromDEM differencing

Velocity

DisplacementVectors

Glacier products

Surface albedo from geostationary satellites

MODIS

Lattanzio A.; et al., 2013:

Land Surface Albedo from Geostationary

Satellites: a multi-agency collaboration

within SCOPE-CM, 2013.

Bulletin of the American Meteorological

Society.

Geo Sats

Geostationary satellites have

better temporal sampling, hence a

higher probability to obtain a clear

sky view

7/31/201717

Climate Impact on Marine Ecosystem StateImpact of El Niño variability on ocean primary producers

Impact on Phytoplankton Indicators:• Chlorophyll• Primary Production• Phenology (bloom timing, growth duration)

Change in forcing:• Climate Index• El Niño variability

Primary Production

0

+10

-10

% change

Growth duration

0

+50

-50

% change

Racault et al., In prep. ESA Living Planet Fellowship 2015-2017 Capotondi et al., JAS 2014

Eastern Pacific El Niño

Central Pacific El Niño

The impact on ocean primary producers is different in different regions for the two types of El Niño

ENVISAT

ENVISAT vs Sentinel 1A

Sentinel 1A+B

ENVISAT vs. Sentinel-1 coverage of flood prone area : the Danube river (5-day simulation)

Ozone layer depletion recovering

Ozone hole

Montreal Protocol outcome

Slightly positive trend of

global ozone increase of

almost 1% per decade in the

total ozone from the last 14

years(result confirmed by comparisons with

ground-based measurements)

Mean sea level rise(ESA CCI/CLS/LEGOS)

Global SLR (+/- 10mm variation about mean) (ESA Climate Change Initiative)

Sea Surface Temperature- ESA Climate Change Initiative

ESA/PB-EO/166/RoomDoc(2016)21

UNFCCC Paris Agreement 2015

COP-21 and relevanace to ESA EO programmes | Slide 26ESA UNCLASSIFIED - For Internal Use

Perception?

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UNFCCC Paris Agreement–How can observations support the Agreement

• Support stocktaking methods by provision of anthropogenic

emissions data – CO2, land use change, etc…

• Support mitigation measures e.g. REDD+, land use change

• Provide improved indicators of change and support definition of

risk factors and indicators

• Guide future policy by providing data on effectiveness

of implementations of adaptation measures

• Continue to provide data to support disaster risk reduction

• Support indicators and targets of Sustainable Development Goals

related to climate change (many of them)

• Further support research in climate change by enhanced data

provision and support development of climate services

• Etc..

• BUT be cautious about ensuring political leadership

OCO-2 CO2 concentrations (NASA-JPL)

Atmospheric Methane (CH4) is Increasing

• Third most abundant greenhouse gas after H2O and CO2

• Grown by 150% since pre-industrial era (in 1750 it was 700 ppb)

• Less abundant than CO2 but more efficient as a greenhouse gas

• Relative to CO2 it contributes about 60% in terms of radiative forcing

Source World Economic Forum Global Risks Report 2016 Copyright World Economic Forum 2016

http://www.weforum.org/reports/the-global-risks-report-2016

Global Risks Landscape 2016

Failure of

climate-change

mitigation and

adaptation

Source World Economic Forum Global Risks Report 2017 Copyright World Economic Forum 2016

http://www.weforum.org/reports/the-global-risks-report-2017

Global Risks Landscape 2017

Extreme Weather

&

Natural Disasters

Increased likelihood of 2003 European heatwave due to anthropogenic change (Christidis et al. 2015)

Posterior likelihoods change faster than basic climate parameters

European example – progressive likelihood of a standard >1.6K heatwave:

• 1900: 1 in 100 years

• 1990-2000: 1 in 50 years (Stott et al, 2004)

• 2005-2010: 1 in 5 (Christidis et al., 2015)

• (2025: 1 in 2, 2040: 1 in 1, (depending on scenarios))

While Ts “only” increased by about 0.8C in period 1900-2010.

Normalised future summer temperature anomalies (ibid.)

Non-linear dependence of specific outcomes (King et al. 2015)

COST OF MORTALITY OF PM2.5 EXPOSURE

OMI, 2007 OMI, 2008

NO2 reductions detectedfrom space during the 2008Beijing Olympic GamesMijling et al., Geophys. Res. Lett. (2009)

Monthly OMI NO2over Beijing

Witte et al. (2009)

Climate observations and data

analytics

Requires plentiful data, datasets as complete as

possible

all data items must to be tagged with a unique identifier

(x,y,z,t,…..) together with specific information

May be helpful when processes are too complex to be

modelled, or even unkown, and when data structures

are not a priori identifiable

Can in some cases examine questions relating to

societal risk much better than Bayesian methods

Show promise

Test: can analytics techniques (re)discover known

structures in data, and hence known causal relationships

in relation to climate impacts?