The Global Energy and Water Exchanges (GEWEX) project, as part of the World Climate Research Programme (WCRP), launched a new initiative called "Impact of initialized land temperature and snowpack on sub-seasonal to seasonal prediction" (LS4P) in 2018.
"More than forty research groups worldwide participated in the LS4P project and it's like an exciting competition as well as collaboration. Now we are in the first phase. We want to pursue a new gateway in improving the sub-seasonal to seasonal (S2S) prediction with the application of the land surface and subsurface temperature approach. We call it LST/SUBT approach." Introduced Dr QIU Yuan from Institute of Atmospheric Physics, Chinese Academy of Sciences.
The first phase experiment of LS4P is to simulate the effect of spring LST/SUBT anomalies over Tibetan Plateau on late spring and summer precipitation in East Asia with multiple earth system models. Each model group conduct a control and sensitive experiment. Compared with the control experiment, the only difference in the sensitive experiment is that the LST/SUBT initial anomalies calculated based on the observed 2-meter air temperature (T2m) anomalies and model bias is imposed over Tibetan Plateau.
However, Dr. QIU found his model and other LS4P models are generally unable to preserve the imposed LST/SUBT anomalies well and thus have difficulty in generating the observed T2m anomalies over Tibetan Plateau.
"We suspect that the current models' deficiencies in maintaining the LST/SUBT initial anomalies are mainly rooted in their land parameterizations." said Dr. QIU. They assessed the memory of LST/SUBT initial anomalies over Tibetan Plateau in three widely used land models (SSiB, CLM4, and Noah-MP) by imposing ±5℃ initial anomalies in the sensitive experiment. The memories of the LST/SUBT initial anomalies (referred as surface/soil memories) are defined as when time series of the differences in daily LST/SUBT between the control and ±5℃ experiments cross the zero line, with the unit of days. The memory of T2m anomaly (referred as T2m memory) is defined in the same way, as an index to assess the response of surface air temperature to the LST/SUBT initial anomalies.
"The results show that the simulated soil memory generally increases with soil depth and it changes rapidly with depth above about 0.6-0.7 m." said Dr. QIU. "The land models have fairly long soil memory, with the regional mean 1.0-m soil memory generally longer than 60 days. However, they have short T2m memory, with the regional means generally below 20 days. This may bring a big challenge to use the LST/SUBT approach on the S2S prediction."
Dr. QIU found that CLM4 and Noah-MP have longer soil memory at the deeper layers (> ~0.05m) while SSiB has longer T2m/surface memories and near-surface (≤ ~0.05m) soil memory, which makes it hard to say which land model is optimal for the application of LST/SUBT approach. Unexpectedly, the T2m/surface/soil memories are various over Tibetan Plateau, different between the +5℃ and -5℃, and distinct among the land models, which can be partially explained by both changes in the surface heat fluxes and variances in the hydrological processes over the plateau.
"This study can help us understand the LST/SUBT approach and provide critical thoughts and methods to further LS4P experiments and the development of land models." Said Dr. QIU.
Time series of the differences in daily soil temperature at the layer of 0.25 m between the control and +5℃ experiment with the land model of Noah-MP on a randomly selected model grid (35.3°N, 88.4°E). The three-point smoothing method is applied to erase the high-frequency oscillations in the time series. (Image by IAP)
Qiu, Y., Feng, J., Wang, J. et al. Memory of land surface and subsurface temperature (LST/SUBT) initial anomalies over Tibetan Plateau in different land models. Clim Dyn (2021). https://doi.org/10.1007/s00382-021-05937-z