Der$Ozean$im$globalen$Wandel:$ wärmer,$saurer,$atemlos$ · wärmer$ saurer$ atemlos$ sinkende$Produk]vität steigende$Produk]vität Gruber2011 korrosivfür$ Aragonit Sauerstoﬀmangel$

Der Ozean im globalen Wandel: wärmer, saurer, atemlos

Ulf Riebesell

GEOMAR Helmholtz-‐Zentrum für Ozeanforschung Kiel

FONA-‐Forum, Leipzig, 9.-‐11. 09. 2013

wärmer Oberflächentemperatur

saurer pH Wert

atemlos Sauerstoffgehalt

Der Ozean im globalen Wandel:

Historisch RCP2.6

Stabilisierung bei 450 ppm CO2

RCP8.5 „business-‐as-‐usual“


Bopp et al. 2013

0

wärmer saurer atemlos sinkende Produk]vität

steigende Produk]vität Gruber 2011

korrosiv für Aragonit Sauerstoffmangel

Der Ozean im globalen Wandel


Hotspots des Ozeanwandels

Kalifornien-‐Strom

Humboldt-‐Strom

Kanaren-‐Strom

Benguela-‐Strom

Somali-‐Strom

•  Kongruenz der globalen Stressoren (wärmer, saurer, atemlos) •  hohe Produk]vität •  Schlüsselregionen für biogeochemische Stoffumsätze •  starke Klimarückkopplung •  hohe ökonomische Relevanz à 30% des globalen Fischfangs

Aucriebssysteme: Hotspots im globalen Wandel


CO2 E

miss

ionen

(Gt C

/Jahr

)

Fossile Energieträger

Veränderte Landnutzung

10

8

6

4

2

1960 2010 1970 1990 2000 1980 Jahr 1 Gt = 1 Gigatonne = 1 Milliarde Tonnen

CO2 Emissionen (1960-‐2009)

Global Carbon Project 2010


Ursache: anthropogene CO2 Emissionen

Global Carbon Project 2012

1.1±0.7 Gt C/Jahr

+7.7±0.5 Gt C/Jahr

2.4 Gt C/Jahr

27%

4.1±0.1 Gt C/Jahr

47%

26% 2.3±0.4 Gt C/Jahr

Global Carbon Project 2012; Angaben sind Mittel für den Zeitraum 2000-2010

CO2 + H2O à HCO3- + H+

[H+] à pH

Wo bleibt das zusätzliche CO2? Ursache: anthropogene CO2 Emissionen


1 Gt = 1 Gigatonne = 1 Milliarde Tonnen

Mixed layer depth Sinking

Grazing

Dienstleistungen des Ozeans

Nahrungsnetze und Stogreisläufe

Biodiversität Biogeographie Ökosysteme

Fischerei-‐Erträge Wirkstoffe aus dem Meer

Stoff-‐ und Energiekreisläufe

Tourismus Küstenschutz

CO2 Speicherung ProdukJon von Klimagasen

Globaler Wandel

Physik u. Chemie der Ozeane


Einfluss auf Organismen

(nega]v und

posi]v)

Mixed layer depth Sinking

Grazing

Dienstleistungen des Ozeans

Nahrungsnetze und Stogreisläufe

Biodiversität Biogeographie Ökosysteme

Fischerei-‐Erträge Wirkstoffe aus dem Meer

Stoff-‐ und Energiekreisläufe

Tourismus Küstenschutz

CO2 Speicherung ProdukJon von Klimagasen

Globaler Wandel

Physik u. Chemie der Ozeane


Einfluss auf Organismen

(nega]v und

posi]v)

•  Wechselwirkung mul]pler Stressoren global: Erwärmung, Versauerung, O2 Abnahme regional: Eutrophierung, Verschmutzung, Überfischung

•  Potenzial zur evolu]onären Anpassung

•  Sensi]vitäten auf Ökosystem-‐Ebene

•  Rückkopplungen zum Klimasystem

•  Sozio-‐ökonomische Konsequenzen

Forschungsdefizite


Selek]on

•  starke Veränderungen der Umweltbedingungen (Kongruenz mul]pler Stressoren)

•  hohe Sensi]vität der Ökosysteme •  starke Rückkopplung zum Klimasystem •  hohe sozio-‐ökonomische Relevanz

Ø  Aucriebsgebiete Atlan]k: Kanaren-‐ und Benguela-‐Strom Pazifik: Humboldt-‐ und Kalifornien-‐Strom Indik: Somali-‐Strom, Arabisches Meer

Ø  Aunau auf laufende Projekte SPACES* (GENUS, SACUS, AGULHAS), CARIMA Ocean Science Centre Mindelo, Kapverden *SPACES -‐ Science Partnership for the Assessment of Complex Earth System Processes

Kriterien für Schlüsselregionen


(Fig. 1). Equatorward winds along the eastern flanks (Figs. 1 and 2)feed the trades and drive the broad and slow eastern boundaryBenguela, California, Iberia/Canary and Humboldt currents. Nearshore (order 25–150 km), interaction with earth’s rotation (Coriolis)and presence of the coastal boundary, produces a shallow (order50 m) wind-driven offshore surface Ekman flow which is replacedby cool and nutrient-rich waters from below; such ‘coastal upwell-ing’ leaves a strong imprint on sea surface temperature and chloro-phyll (Figs. 1 and 2). Over the shelf and slope an undercurrentflows poleward so that average currents within 100 km from thecoast are opposite the surface winds; further offshore the Bengu-ela, California, Iberia/Canary and Humboldt currents flow. Thephysical process of coastal upwelling with its equations has been

described repeatedly (see Allen, 1973; Barber and Smith, 1981a;Smith, 1995; Bakun, 1996; Hill et al., 1998). In addition to thestrength of the upwelling-favorable equatorward winds, water col-umn stratification, coastal topography, and latitude-dependence ofthe Coriolis parameter play an important role; at low latitude,favorable winds produce more upwelling than at high latitude(Table 1). The shallow offshore flow, often termed Ekmantransport, is balanced by onshore subsurface flow beneath the‘Ekman layer’. Planktonic organisms, including larval fish andinvertebrates, can exploit this conveyor belt and be retained inthe coastal productive habitat (Peterson et al., 1979; Barber andSmith, 1981b; Peterson, 1998; Carr et al., 2008). The upwelling-favorable winds typically increase offshore to maximum mean

Fig. 2. Higher resolution maps of SST, winds and chlorophyll for each EBUE. SST is MODIS monthly averaged from July 2002 to April 2008; chlorophyll is SeaWiFS monthlyaveraged from September 1997 to September 2007; winds are QuikSCAT weekly averaged from July 1999 to July 2008. All products were regridded on the QuikSCAT grid(0.25! ! 0.25!) prior to averaging. The insert in the chlorophyll map shows the mean primary productivity (PP) and fish catch for the years 1998–2005. It was assumed thatthe reported fish catches (Fish and Agriculture Organization, FAO) were made within 100 km from the coast. Primary productivity was estimated from satellite remotesensing of chlorophyll and the Behrenfeld and Falkowski (1997) model. Peru fish catch exceeds that from the other areas by an order of magnitude even though PP levels aresimilar.

Table 1Comparison of annual mean properties in a 10! latitude coastal (0–150 km) band for the four EBUE. Winds are from QuikSCAT (0.25! ! 0.25!, 8-day, 1999–2008, provided by PFEL,NOAA). Calculations for upwelling, mean nitrate concentration at 60 m and the potential new production are described in Messié et al. (in this issue). Vertical upwelling rateswere calculated by dividing Ekman transport by the Rossby radius of deformation (obtained from Chelton et al., 1998) and for pumping by dividing by the 150 km band;turbulence is given as the cube of the wind speed. Chlorophyll concentration, 1 mg m"3 chl distance from shore, and PAR are calculated from SeaWiFS (9-km degraded on0.25! ! 0.25!, geometric mean); primary production was calculated following Behrenfeld and Falkowski (1997). Mixed layer depth were calculated from in situ temperatureprofiles following the Naval Research Laboratory Mixed Layer Depth (NMLD) methodology (depth where temperature = temperature at 10 m " 0.8 !C). Dust deposition wascalculated from the model of Mahowald and Luo (2003).

Benguela California NW Africa Peru28!S–18!S 34!N–44!N 12!N–22!N 16!S–6!S

Coriolis parameter f (s"1) "5.7e"05 9.18e"05 4.26e"05 "2.78e"05Area (1011 m2) 1.67Average wind speed (m s"1) 7.2 7.8 6.8 5.7Upwelled volume (Sv) 1.5 1.0 1.4 1.6Average vertical speed by ektrans (10"5 m s"1) 3.18 3.30 2.35 1.55Average vertical speed by ekpump (10"5 m s"1) 0.32 0.18 0.36 0.52Percentage ektrans/total 77.0 77.5 74.9 68.6Average [NO3] at 60 m (lmol L"l) 16.9 14.9 19.0 16.8Potential new production (g C m"2 yr"1) 517 323 539 566SeaWiFS primary production (g C m"2 yr"1) 976 479 1213 855SeaWiFS chlorophyll (mg m"3) 3.1 1.5 4.3 2.41 mg m"3 Chl extension (km) 160 49 164 120Turbulence (m3 s"3) 444 610 371 225MLD (m) 44.8 43.3 28.9 30.7PAR (E m"2 d"1) 43.1 34.0 47.6 43.3Dust deposition (g m"2 yr"1) 11.7 0.3 33.1 0.3

82 F.P. Chavez, M. Messié / Progress in Oceanography 83 (2009) 80–96








Primärproduk]on

Fischerei Erträge x 100 ( g

C / m

2 /d )

Kanaren

Humboldt








(Chlorop

hyll con

centra]o

n (m

g m

-‐3)

Benguela

Humboldt

•  globales Monitoring-‐Netzwerk für Ozeanwandel

(interna]onales Netzwerk im Aunau)

•  systemare Studien in Hotspot-‐Regionen (Beobachtungen – Experimente -‐ Modellierung)

•  enge Verzahnung mit sozio-‐ökonomischen Studien (Fischerei, Tourismus, Küstenschutz, ...)

•  Kommunika]on (Einbindung von Interessensgruppen, Informa]onsfluss an Entscheidungsträger und Öffentlichkeit)

•  Koopera]onsbeziehungen mit Anrainerstaaten (inklusive capacity building)


Komponenten eines Forschungsverbunds

996 M. Steinacher et al.: Projected decrease in marine productivity

Fig. 10. (a) Multi-model mean of vertically integrated annual mean PP under preindustrial conditions (decadal mean 1860–1869) and(b) projected changes by the end of the 21st century under SRES A2. The changes are shown on an exponential scale and represent thedifference between 2090–2099 and 1860–1869 (decadal means). The multi-model means have been computed by using the regional skillscores shown in Fig. 9 as weights. The dotted areas indicate that none of the regional skill scores is higher than 0.5. Where no observation-based data is available to calculate skill scores (e.g. in the Arctic) the arithmetic mean of the model results is shown.

The weights w(x,y)i,j used here are the same as givenabove. The multi-model mean with this second metric is cal-culated as

PPEi,j =

⇥

m

E�1m,i,j�

mE�1m,i,j

PPm,i,j . (7)

In addition, we have computed the arithmetic mean fromall models (PPave) as well as the mean obtained by weight-ing individual models with their global (� = ⇤) skill score(PPSglob ).Next, global skill scores (Sglob) and global root mean

square errors (RMSE) are computed for the individual modelresults and for the multi-model fields obtained by the fourdifferent averaging methods (Table 1). The global skill scorefor the first field (PPS) is considerably higher than for theothers. All averaging methods result in a lower global skillscore than that of the two best models (IPSL and CCSM3).However, the RMSE is lower for the PPS field than for eachindividual model and for the other multi-model fields. In thefollowing, we discuss results from this metric only. We notethat differences in the results obtained by the first two metrics(PPS and PPE) are generally small.This skill score method accounts for the different skills

of the models at reproducing regional features of the satel-lite based estimates, while not degrading the overall skill inrepresenting the satellite-based field compared to the best in-dividual model. For example, the CSM1.4 model reproducesthe high PP tongue around 40⇥ N in the North Atlantic. TheIPSL model captures most of the high PP features along thecoasts of South America and Africa. The MPIM model hasa high skill in the central Pacific and the most realistic latitu-dinal extension of the equatorial PP belt, while the CCSM3

captures best the magnitude and pattern of PP around 40⇥ S.Therefore these models dominate the mean in those regions(Fig. 9d), and all these features are present in the multi-modelmean (Fig. 10a). There remain weaknesses. All models un-derestimate PP in the Arabian Sea and off the west coast ofNorth America. Consequently, the multi-model mean alsomisses these features. Overall, this method improves themulti-model mean significantly compared to simpler averag-ing methods (Table 1).Regional skill scores are applied to calculate the multi-

model mean of preindustrial PP and of the projected changesby the end of the 21st century (Fig. 10) and as a function ofthe global mean surface air temperature (SATglob, Fig. 11d).The globally integrated annual mean PP decreases from37.1GtC yr�1 (preindustrial) to 33.0Gt C yr�1 by 2100 AD(�2.9Gt C yr�1; �8%) for the multi-model mean (Fig. 1,Table 1). Large decreases in PP are projected for the NorthAtlantic, off the coast of Africa in the South Atlantic, in thePacific around the equator and around 30⇥, and in the north-ern part of the Indian Ocean; a slight increase in PP is foundin the Southern Ocean and in the Arctic (Fig. 10b). Calculat-ing the mean by 2100 has the disadvantage that PP changesare merged that correspond to different temperature changesas the models have different climate sensitivities. One way toavoid this is to calculate the regression slope ⌅PP/⌅SATglobfor each grid cell (Fig. 11a–c) as done for the global PP inFig. 1c. The patterns of the resulting PP change per centi-grade SAT increase are broadly consistent with the patternsof the projected PP change by 2100.

Biogeosciences, 7, 979–1005, 2010 www.biogeosciences.net/7/979/2010/


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Der$Ozean$im$globalen$Wandel:$ wärmer,$saurer,$atemlos$ · wärmer$ saurer$ atemlos$ sinkende$Produk]vität steigende$Produk]vität Gruber2011 korrosivfür$ Aragonit Sauerstoﬀmangel$