Future climate scenarios for phosphorus and nitrogen dynamics in the Gulf of Riga

Bärbel Müller-Karulis, Latvian Institute of Aquatic EcologyJuris Aigars, Latvian Institute of Aquatic Ecology

In colaboration with Juris Seņņikovs, Laboratory for Mathematical Modelling of Environmental and Technological Processes, Faculty of Physics and Mathematics, University of Latvia

Latvian Institute of Aquatic Ecology

Gulf of Riga characteristics

• Semi-enclosed basin• Connected to Eastern Gotland

basin surface waters• Salinity 5.5 – 6.2 PSU• Shallow: average depth 22 m,

maximum 56 m• no permament halocline,

seasonal thermocline, monomictic circulation

• High freshwater and riverine nutrient input

• Regular monitoring since 1973

Response to climate change

?

Mean temperature (period 1973-2008)

Mean oxygen (period 1973-2008)

Model setup• Biogeochemical model

– phytoplankton, zooplankton and nutrients

– NPZD Box model based on Savchuk 2002

– 2 boxes (pelagic, demersal) + sediments

– 3 phytoplankton groups– Zooplankton– NO3, NH4, PO4, O2

– Calibrated to 28-year observation series

• Physical model (1D)– vertical temperature

distribution in the GoR– General Ocean Turbulence

Model (GOTM)– Coefficients of second

order model: Cheng (2002)– Dynamic equation (k-ε

style) for TKE– Dynamic dissipation rate

equation

Temperature changeEcosystem response

Ins titute Model Driving data Ac ronym E xperiment

S MHI R C AO high res . HadAM3H A2 HC C T L _22 control

S MHI R C AO high res . HadAM3H A2 HC A2_22 scenario

Ins titute Model Driving data Ac ronym E xperiment

S MHI R C AO high res . HadAM3H A2 HC C T L _22 control

S MHI R C AO high res . HadAM3H A2 HC A2_22 scenario

Climate data from PRUDENCE. Control: 1961-1990, Scenario A2: 2070-2100

Extra downscaling of RCM data (bias correction via histogram equalisation):

relative humidity (used variable td2m) air temperature (used variable t2m)

Original RCM data:sea level pressure (used variable MSLP)cloudiness (used variable clcov)wind speed (used variable w10m)wind direction (used variable w10dir)

Calculations made for Gulf of Riga (50 m), 30 year period, daily output data – water temperature

Climate change scenario

Physical model results – I(mean temperature distribution over depth)

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

0 1 2 3 4 5 6 7 8 9 10 11 12

Temperature, degC

Dep

th, m

Water 1961-1990

Water 2071-2100

Air 1961-1990

Air 2071-2100

T increase by 1,5 (bottom) to 3 (surface) degrees

Surface T increase close to air T increase

Physical model results – II(mean daily pycnocline depth and its

variation)

-60

-50

-40

-30

-20

-10

0

Jan Feb Mar Apr Mai Jūn Jūl Aug Sep Okt Nov Dec

Dep

th, m

1961-1990

2071-2100

Pycnocline develops earlier...

... stratification lasts longer

Physical model results – III(mean time-depth plots of temperature)

Contemporary climate

Climate change scenario A2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

md-1

Vertical water exchange

CTL

A2

Vertical water exchange

decreased vertical water exchange

Demersal oxygen – GR mean

4

5

6

7

8

9

10

11

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

ml l

-1

Demersal oxygen CTL

A2

Species succession

0

20

40

60

80

100

120

140

160

180

200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

mg

C m

-3

Spring phytoplankton CTL

A2

earlier and more vigorous spring bloom

Species succession

0

50

100

150

200

250

300

350

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

mg

C m

-3

Summer phytoplankton CTL

A2

larger biomass of summer species

0

2

4

6

8

10

12

14

16

18

20

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

mg

C m

-3

Nfixing phytoplankton CTL

A2

Species succession

more cyanobacteria

0

2

4

6

8

10

12

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

mm

ol m

-3

Pelagic NO3

CTL

A2

Pelagic nutrients

larger winter pool

earlier depletion

0

2

4

6

8

10

12

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

mm

ol m

-3

Demersal NO3

CTL

A2

Demersal nutrients

larger demersal nutrient concentrations in summer

0

2

4

6

8

10

12

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

mm

ol m

-2 d

-1

net vertical DIN fluxCTL

A2

Vertical nutrient flux

increased demersal concentrations compensate stratification effect

0

2

4

6

8

10

12

14

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

mm

ol m

-2 d

-1

Pelagic DIN regeneration CTL

A2

Nutrient regeneration

Pronounced increase in nutrient recycling!

Denitrification

Nitrate uptake/release (oxygen control)

Ammonium versus nitraterelease

-500 0 500 1000 1500 2000 2500 3000-800

-600

-400

-200

0

200

400

R² = 0.843512645722358

NH4 µmol m-²d-¹

NO

3 µm

ol m

-² d-

¹

Mean nitrate (period 1973-2008)

Conclusions

• Increase in phytoplankton growth and primary productivity caused by increased nutrient regeneration

• Slightly increased winter nutrient concentrations• Earlier spring bloom (earlier stratification, no ice cover)• Larger summer phytoplankton biomass, more

cyanobacteria because of more intensive nutrient regeneration

• Lower demersal oxygen concentration caused by more stable and longer stratification

• Denitrification – open question for future research