A-WP3 – PastLand II

Summary and structure:

Predictions of the global climate depend on both the model's initial state and the anticipated change in aerosols and greenhouse gases; for decadal predictions anthropogenic climate change and natural variability are expected to be equally important. A close representation of the observed climate state in global coupled climate models is therefore crucial for (the initialisation of) decadal predictions. However, predictability beyond two weeks is essentially influenced by time scales which are longer than typical scales of weather phenomena. These slow components which affect seasonal to decadal predictability of the Earth system are beside the oceans, glaciers and sea ice, the moisture content of the soil, snow cover and the terrestrial biosphere.

During its first phase, Pastland I was active in the land surface model development to allow for a more realistic representation of soil moisture memory, the evaluation of state of the art soil moisture observations as well as conducting experiments to identify regions and time scales of land surface memory. Thus, the necessary prerequisites were fulfilled to allow for the assimilation of land surface observations into the Earth System Model of the Max Planck Institute for Meteorology (MPI-ESM).
This objective is the major task during the second phase of Pastland. An assimilation scheme which is already used in the ocean component of MPI-ESM will be adapted for the land surface and modified to assimilate several land surface observational streams. The best suitable land surface variables will be identified by extending the evaluation analysis done in Pastland1 for further variables like surface or soil temperature and snow cover. The added value of land surface initialisation can then be analysed in hindcast ensemble simulations with the MPI-ESM. Finally, Pastland II will evaluate whether or not there are improvements in the model's predictive skill that justify the effort of the additional assimilation.
 

Objectives:

  • Identify land surface variables beside soil moisture that contribute to overall land surface memory and predictability in simulation and observation
  • Evaluate state of the art land surface observations in respect to their potential suitability for land surface assimilation
  • Adapting the MiKlip assimilation and initialisation routines for the assimilation of several land surface data streams
  • Evaluate the prediction skill for the MiKlip Prediction System with and without land surface assimilation

 

Deliverables:

  • Database of land surface observations suitable for assimilation
  • Land surface assimilation and initialisation scheme
  • Decadal hindcast simulations

 

Progress so far

Recently, the prototype version of the land surface assimilation scheme was developed and implemented into JSBACH, which is the land surface component of the MPI-ESM. From a technical point of view, a major requirement for the scheme was a self-contained structure with as few interactions with the model as possible. This facilitates an easy and fast implementation into the MiKlip Prediction System later on. Furthermore, the developed module structure allows for a straightforward extension with respect to other land surface variables.

The scheme employs a nudging algorithm following Brocca et al (2010), where top layer soil moisture is updated once per day. This is consistent with the temporal resolution of available observations, while preserving the sub-daily variability generated by the model.

Preliminary results of soil moisture assimilation simulations indicate that the effect of assimilation is strongest on the land surface, e.g. altering the South Asian monsoon index significantly. Over the ocean, its impact is limited to smaller regions and affects the salinity around the outlets of large rivers.

 

Near surface temperature anomaly correlation

Sensitivity simulations assimilating extreme soil moisture conditions demonstrate that the effect of assimilation is strongest on the land surface with distinct impacts on surface temperatures and evapotranspiration. Furthermore some large scale circulation systems are effected, e.g. the South Asian monsoon index changes. Over the ocean, its impact is limited to smaller regions and affects the salinity around the outlets of large rivers.

Assimilating actual observations of satellite based top layer soil moisture and snow water equivalent seems to have just a minor impact on the predictive skill of the MPI-ESM. The anomaly correlation coefficients (ACC) for both variables are positive for the first lead year, both decrease towards insignificant levels in the following years. For near surface temperature, the global ACC pattern are rather similar with and without land surface assimilation. In the first lead year, the predictive skill with land surface assimilation is slightly higher over Eastern Europe and Western Asia while showing a lower value for the regions of East Asia and Alaska. In the following years this differences decline for most areas.

From such results, there is no strong indication that land surface assimilation has a crucial impact on the ESM’s predictive skill, at least on the investigated temporal and spatial scales. However, this will probably change in case reliable estimates of deep soil moisture states become available for long periods.

Figure: Near surface temperature anomaly correlation (ACC) between MPI-ESM hindcasts and HadCRUT4 data. The left panels show the ACC for hindcasts based on initial states from the assimilation of atmosphere, ocean and land surface. The right panels show the difference between these hindcasts and those initialized only with atmosphere and ocean assimilation. The upper panels show results for the first lead year, the lower for the lead years 2 to 5.

References

  • Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., and Hasenauer, S.: Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881-1893, doi:10.5194/hess-14-1881-2010, 2010.

Contact

Max-Planck Institute for Meteorology
Tobias Stacke
tobias.stacke(at)nospammpimet.mpg.de

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