1 1 Morten D. Skogen WP10: Hindcast and scenario studies on coastal- shelf climate and ecosystem variability and change Overview and plans ECOOP annual meeting, Athens 12-15/2, 2008

2 2 WP10: PARTNERS: IMR (coordinator) DMI UiB-GFI NERC PML UNIV-GDA ASSOCIATED: CEFAS SMHI IOW IMI

3 3 WP10: T10.1: Hindcast and scenario studies on coastal-shelf climate and ecosystem variability and change (all) S10.1.1: Quantify the monthly to decadal variability of the shelf seas –coastal physics/climate (IMR, DMI, UiB-GFI, NERC, Univ-GDA) S10.1.2: Quantify the monthly to decadal variability of the climate effects of the lower trophic levels of the shelf seas-coastal ecosystems (IMR, PML, UiB-GFI, NERC, Univ-GDA) S10.1.3: Quantify the potential effects on shelf seas-coastal climate and ecosystem from global climate change predictions (IMR, DMI, Univ-GDA) S10.1.4: Quantify the potential effects on shelf seas-coastal ecosystems due to management scenarios and related to natural variability (IMR, DMI, UiB-GFI, NERC, Univ-GDA) S10.1.5: Produce multi-decadal reference databases and monthly climatologies of modelled shelf seas-coastal climate and ecosystems (IMR, UiB-GFI, NERC) T10.2: WP10 Coordination (IMR)

4 4 WP10: S10.1.1: Quantify the monthly to decadal variability of the shelf seas –coastal physics/climate (IMR, DMI, UiB-GFI, NERC, Univ-GDA) Objective: perform the first 3-dimensional simulations of the last years of temperature, salinity, turbulence and currents. Achieve relevant observations for validation of precision and accuracy. Define analysis methodology to be performed and specific products to be delivered. D (Mo18): First year climate simulations ready. D (Mo30): Report on the past years climate variability

5 5 WP10: S10.1.1: Quantify the monthly to decadal variability of the shelf seas –coastal physics/climate (IMR, DMI, UiB-GFI, NERC, Univ-GDA) AREAResolutionPeriodPartnerComment North Sea/Baltic9km Univ-GDA North Sea/Baltic2km Univ-GDAExtension ongoing North Sea/Baltic10km UiB-GFI North Sea/Baltic10km UiB-GFI Barents Sea7km UiB-GFI North Sea20km IMR North Sea10km IMR North Sea/Baltic6nm-1nm DMIPlanned

6 6 WP10: S10.1.1: Quantify the monthly to decadal variability of the shelf seas –coastal physics/climate (IMR, DMI, UiB-GFI, NERC, Univ-GDA) Baltic Sea (upper) and North Sea (lower) heat content anomaly as calculated by ECOSMO. Blue lines: monthly anomaly, red line: moving average annual window; mean seasonal signal removed.

7 7 WP10: S10.1.2: Quantify the monthly to decadal variability of the climate effects of the lower trophic levels of the shelf seas-coastal ecosystems (IMR, PML, UiB-GFI, NERC, Univ-GDA) Objective: perform the first 3-dimensional simulations of the last years of primary production (PP), concentration of functional groups of algae, nutrients and bottom oxygen and sedimentation. Achieve relevant observations for validation of precision and accuracy. Define analysis methodology to be performed and specific products to be delivered. D (Mo18): First year simulations of PP ready. D (Mo30): Model to model PP variability study. D (Mo36): Report on the past year PP variability

8 8 WP10: S10.1.2: Quantify the monthly to decadal variability of the climate effects of the lower trophic levels of the shelf seas-coastal ecosystems (IMR, PML, UiB-GFI, NERC, Univ-GDA) AREAResolutionPeriodPartnerComment Baltic-Gulf of Gdansk5nm-1nm Univ-GDA North Sea/Baltic10km UiB-GFI North Sea10km IMR Nordic and Barents Seas20km IMR

9 9 WP10: S10.1.2: Quantify the monthly to decadal variability of the climate effects of the lower trophic levels of the shelf seas-coastal ecosystems (IMR, PML, UiB-GFI, NERC, Univ-GDA) Special coefficients of correlation in the function of total quadratic error for whole profiles state variables at the station P1 a), all stations b) and terms for all measurements at c) surface layer d) open boundary Gulf of Gdańsk from the period

10 WP10: S10.1.3: Quantify the potential effects on shelf seas-coastal climate and ecosystem from global climate change predictions (IMR, DMI, Univ-GDA) Objective: downscale the predicted future climate to shelf seas by running the models used in Task 10.1 for several years several decades into the future with forcing from the coupled climate prediction models, and produce similar monthly averages as in Task D (Mo17): Spec. of potential common climate scenarios D (Mo24): Downscaling simulations of the future climate D (Mo36): Downscaling simulations of the future PP ready and reported together with climate

11 WP10: S10.1.3: Quantify the potential effects on shelf seas-coastal climate and ecosystem from global climate change predictions (IMR, DMI, Univ-GDA) AREAResolutionPeriodPartnerComment North Sea7km & IMROnly physics The SRES A1B scenario for the period 2072–2097 with the Bergen Climate Model (BCM) has been downscaled for the marine climate in the North Sea using the Regional Ocean Model System (ROMS). The results are compared to the 20C3M run for the period 1972–1997.

12 WP10: S10.1.3: Quantify the potential effects on shelf seas-coastal climate and ecosystem from global climate change predictions (IMR, DMI, Univ-GDA) Seasonal cycle of averaged temperature in the North Sea for the 20C3M and A1B run respectively. The averaging periods are 1972–1997 for 20C3M and 2072–2097 for A1B. Average increase: 1.4 o C.

13 WP10: S10.1.4: Quantify the potential effects on shelf seas-coastal e cosystems due to management scenarios and related to natural variability (IMR, DMI, UiB-GFI, NERC, Univ-GDA) Objective: define what-if scenarios to be simulated, define analysis to be done and prepare for simulations. D (Mo24): Demonstrate effects of reduced nutrient loads on PP in the North Sea D (Mo30): Model to model intercomp. of what-if scenarios Subtask started out in month 13

14 WP10: S10.1.5: Produce multi-decadal reference databases and monthly climatologies of modelled shelf seas-coastal climate and ecosystems (IMR, UiB-GFI, NERC) Objective: The full 3D results from the long-term simulations in S10.1 and 10.2 will be stored with an as high as practical possible time resolution in an easy accessible database. Monthly means and climatologies will also be produced and stored in the same databse D (Mo28): Spec. of a database of long term simulation D (Mo36): Deliver a database of long term model simulations Subtask started out in month 13

15 WP10:CONCLUSION WP10 is in good progress with respect to all deliverables Bergen seen from Mt. Ulriken