sensitivity of atmospheric near-land temperature in europe to sst

Sensitivity of atmospheric near-land temperature in Europe to SST

Andrey Vlasenko, Armin Köhl, Detlef Stammer

Institut für MeerskundeUniversität Hamburg

Hamburg

TASK

Task 1.2.1 Identification of the atmospheric response to ocean surface state changes

The EU FP7 THOR adjoint assimilation system will be used to identify sensitivities of predictable elements over northern Europe, such as air temperature or precipitation, on parameters in the North Atlantic and the Arctic, such as SST, sea surface salinity or sea ice thickness and concentration. The sensitivity information will be used subsequently to unravel the processes affecting the important climate parameters over northern Europe and underlying time scalesTask leading to deliverable D18.

Plan of the Experiment 1. Spin up the climate model CESAM, until the proper climatology is established

2. Develop such cost functional that

a) measures the atmospheric near-land temperature in Europe

b) gives the gradient with minimum numerical noise during the adjoin computations

3. Using TAF AD tool, obtain the adjoint of CESAM

4. Develop a set of filters that remove numerical noise without spoiling the result

The Model

CESAM = PLASIM(atmosphere) + MITgcm(ocean)

The Driver Program

CESAM

Initialize Atmosphere and Ocean

The Main Loop

Postprocessiong

Interpolator

RESULT

MITgcm ocean(1 step)

PLASIM Atmosphere (10 steps)Interpolator

The ExperimentSETUP: 1. Grid resolution

a) In atmosphere is T21 with 10 vertical layersb) in ocean is with 15 vertical layers.

2. Time step: in ocean is equal to 8 hours in atmosphere is equal to 48 minutes.

TASK: Compute

Cost Functional:

Where , is temperature, is spatial coordinate, time steps, is a point in the middle of Europe, has a value that temperature values outside Europe have negligible impact in .

Adjoint:The gradient of (adjoint of CESAM) was generated by a special algorithmic differentiation tool TAF. 𝐽

Problem 1

The unstable mode.

Problem 2

Distribution of the interpolation error, appearing during coupling

Problem 3

Distribution of the values of adjoit of SST represented as histogram

Solutions Implementation of a cascade filtering:

1. Low-pass filter. An unstable mode, resulting in exponential increase of high frequency,

appears during estimation of adjoints. This mode can be removed without affecting the

data by applying low pass filter.

2. Grid noise removal filter. The noise appears due to truncation/approximation errors in

the coupling routine. The pattern of these errors are almost constant in space and time

and therefore can be easily recognized and subtracted from the data.

3. Histogram filter. As above, due to errors on the coupling stage a couple of outliers

appear near the sharp boundaries of the continents. The magnitude of outliers are

several order bigger than the magnitude of the data. Therefore, they can be easily

designated and removed by a histogram filter.

Results

Sensitivity of near surface atmospheric temperature on 15-th of February in Northern Europe to SST 16 hours before the end of the target period.

Results

Sensitivity of near surface atmospheric temperature on 15-th of February in Northern Europe to SST 48 hours before the end of the target period.

Comparison with the results of A. Czasa and C Frankignoul*

SST regression maps showing the tripole (in K, gray shading, dashed contours for negative) and the North Atlantic horseshoe patterns (thick contours, every 0.1 K, dashed for negative).

* A. Czasa and C Frankignoul: Observed Impact of Atlantic SST Anomalies on the North Atlantic Oscillation . J. Cli. (15) 2002.

Results

Sensitivity of near surface atmospheric temperature on 15-th of February in Northern Europe to SST 150 hours before the end of the target period.

Conclusions

1. The sensitivity of atmosphere to SST in a framework of Coupled model can be estimated.

2. It was shown that SST affects the atmosphere in Europe on short time scales of about 1

day mainly kinematically via heating or cooling the air temperature above.

3. On longer time scales of about a few days, dynamic effects become more relevant.

4. Optimal patterns resemble the regression patterns SST/NAO, suggesting that SST most

efficiently affects the atmospheric circulation by triggering an NAO type response.

Further Development

• To obtain the atmospheric response to SST in a framework of maximum configuration

where all processes associated with moisture are included.

• To estimate the atmospheric sensitivity to other oceanic and atmospheric state variables

particularly geopotential height and surface pressure.

Thank you

The research leading to these results has received funding from the European Union 7th Framework Programme (FP7 2007-2013), under grant agreement n.308299NACLIM www.naclim.eu