OPEC Annual Meeting Zhenwen Wan. Center for Ocean and Ice, DMI, Denmark

Transcription

1 OPEC Annual Meeting 2012 Zhenwen Wan Center for Ocean and Ice, DMI, Denmark

2 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups and coupling to SMS, nearly done, by Zhenwen, Eva and Asbjørn T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

3 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups, nearly done, by Zhenwen and Eva T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

4 Task 2.1 Assembly of boundary conditions and forcing functions DMI provided atmospheric forcing from a common regional climate hindcast ( ) for four European regional seas, namely the N.E. Atlantic (PML), the Baltic Sea (DMI), the Mediterranean Sea (HCMR) and the Black Sea (METU). Tian Tian

5 The hindcast simulation was driven with the ERA-interim reanalysis data during the period , performed as a poor man s reanalysis. It has a spatial resolution of 12-km (0.11 degree) with grids 452 (zonal) by 432 (meridional) in the European CORDEX-like domain Tian Tian

6 Riverine input 58 rivers applied in the Baltic Sea. Daily river run-off and nutrient loads (DIN and DIP) from the HYPE model reanalysis ( , Tian Tian

7 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups, nearly done, by Zhenwen and Eva T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

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10 Figure 2 Surface DIN

11 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups and coupling to SMS, nearly done, by Zhenwen, Eva, Asbjørn T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

12 Maar M., E.F. Møller, J. Larsen, K.S. Madsen, Z. Wan, J. She, L. Jonasson, T. Neumann, Ecosystem modelling across a salinity gradient from the North Sea to the Baltic Sea, Ecological Modelling, Volume 222, Issue 10, 24 May 2011, Pages

13 New light attenuation Improved Chl. a

14 New light attenuation Improved DIP

15 New light attenuation Improved Chl.

16 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups, nearly done, by Zhenwen and Eva T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

17 Control Reanalysis All Levels Bias:: RMSE::

18 Control Reanalysis All Levels Bias:: RMSE::

19 Fu et al, 2012 Ocean science

20 Task 2.5 Data assimilation Sub-task: a direct usage of satellite-derived light attenuation coefficient Kd in the DMI-ERGOM Monthly mean Kd-sat (marcoast.dmi.dk) was linearly interpolated to daily fields in The results are compared with a simulation with parameterized Kd-par (by Zenwen Wan) against observations at 1-18 stations. Observation at Station 10 in 2007 is missing. Tian Tian

21 Chl-a (mg/m3) Tian Tian

22 Chl-a (mg/m3) Tian Tian

23 Chl-a (mg/m3) Tian Tian

24 PO4 (mmol/m3) Tian Tian

25 PO4 (mmol/m3) Tian Tian

26 PO4 (mmol/m3) Tian Tian

27 NO3 (mmol/m3) Tian Tian

28 NO3 (mmol/m3) Tian Tian

29 NO3 (mmol/m3) Tian Tian

30 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups, nearly done, by Zhenwen and Eva T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

31 The average profiles of DIN (upper) and Chl (lower)

32 The average profiles of DIN (upper) and DIP (lower)

33 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups, nearly done, by Zhenwen and Eva T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

34 Task 4.2 Baltic Sea seasonal forecast experiments ECMWF seasonal forecast products ( from 11 ensemble members were extracted since The data will be used to provide boundary conditions for DMI high-resolution interactive coupled ocean-atmosphere models in the Baltic Sea. This will ultimately yield 11 ensemble experiments with the 3D biogeochemical model (ERGOM) for 5-month ecological forecasting. Tian Tian

35 Regional coupled ocean-atmosphere model. Atmosphere Ocean Air surface variables Regional HIRHAM Surface air/sea variables coupler SST/ ice Regional ocean-ice HBM Surge model ECMWF multi-model seasonal forecast 11 ensemble members since 2011 Tian Tian

36 Modelled SST as inputs to HIRHAM HBM (11km) + GCM (150km) outside of HBM domain Tian Tian

37 Outlines T2.1 Meta forcing and river loadings. Done, Tian T2.3 Observation data for 20 years. Done, Zhenwen T2.4.1 ERGOM upgrade: including carbon and more zooplankton groups, nearly done, by Zhenwen and Eva T2.5.1 Assimilation experiments: 20 years reanalysis of T/S by Weiwei; assimilating satellite-deriven light attenuation coefficient by Tiantian T year hindcast, going on. T4.1 seasonal forecast, ready to go, Tian T5.2 assessing the current monitoring systems, ready to go, Weiwei

38 Objective: To quantitatively assess the in situ biogeochemical observing system in the Baltic Sea: two indicators effective coverage and explained variance, are defined with parameters obtained from reanalysis data Reference: Høyer, J. L., and J. She, 2007: Optimal interpolation of sea surface temperature for the North Sea and Baltic Sea. Journal of Marine Systems, 65, , doi: /j.jmarsys Fu, W., J. L. Høyer, and J. She, 2011: Assessment of the three dimensional temperature and salinity observational networks in the Baltic Sea and North Sea. Ocean Science, 7,

39 Effective Coverage If two grid cells ( xo, yo, to ) and ( xi, yi, ti ) satisfy: f ( xi xo, y i y o, ti t o ) f c Where fc is the cutting correlation, the two grid cells are called a pair of impact cells, fc can be the e-folding scale ( fc=1/e )

40 T/S Obs network assessment, effective coverage The spatial and temporal scales of the temperature and salinity variations derived in were used to fit spatial and temporal correlation models. Correlation models determined at each layer were used together with the meta data to calculate the effective coverage of the existing observational networks. Effective coverage of the Mediterranean Sea for 10 meters (upper) and 75 meters (lower) depth.

41 Obs network assessment, explained variance Explained variances obtained by applying a multiregression method on the T/S in situ network in the Mediterrenean. Explained variance at the depth of 10m (upper) and 75m (lower) for temperature

42 Things to do in the Baltic Sea The monthly series of observations system for Chl a, DIN, DIP, DO Use the reanalysis data for to calibrate the parameters Issue to discuss At present, the two indicators include parameters, which provide the information of spatial correlation. The parameter derived from reanalysis data may be subject to model deficiencies. Experience with T/S network assessment shows the validity of the method. What about biogeochemical model? the outcome of the assessment is informative. The spatial and temporal gaps existing in the observing network can be identified by the two indicators, but more interpretations are needed to better understand the reasons of the gaps in certain areas.

43 Issue to discuss 20 years hindcast, budgets balance across nutrient pools, with fixed parameters Full coupling or partially coupling with focuses on the target factors

44 Thank you!