[A-AS03] Extreme Events and Mesoscale Weather: Observations and Modeling

[AAS03-04] Increase in subweekly temperature variability over Southern Hemisphere landmasses as observed in multiple reanalyses

*Patrick Martineau¹, Swadhin Behera¹, Masami Nonaka¹ (1.Japan Agency for Marine-Earth Science and Technology)

Keywords:Subweekly variability, Trends, Reanalysis data, Southern Hemisphere

The inter-dataset agreement of trends in subweekly temperature variability near the surface over Southern Hemisphere midlatitude land masses is assessed among twelve major global atmospheric reanalysis datasets. First, a comparison of the climatological temperature variance and dominant sources/sinks of variance reveals that, except for NCEP-NCAR (R1) and NCEP-DOE (R2), there is a relatively good agreement for both their magnitudes and spatial distributions over the satellite era, indicating that the key features of subweekly variability are well represented. Generally, reanalysis datasets that exhibit larger temperature variances also show an amplified generation of variance by horizontal advection and amplified damping by vertical motion. Concerning trends, there is a remarkable agreement for the positive trends in subweekly variability over the satellite era (1980-2022) affecting South Africa in September-October-November (SON), and Southern America in December-January-February (DJF). Although most reanalyses agree concerning the positive trend affecting Australia in SON, it has not yet emerged from the noise associated with interannual variability when considering only the satellite era but is significant when considering the extended period (1954-2022). Over South Africa and Australia, reanalyses that exhibit greater trends in subweekly temperature variability tend to exhibit greater generation of subweekly temperature variance by horizontal temperature advection. Overall, reanalysis datasets agree well concerning the trends, even those that do not assimilate satellite data (JRA-55C) or that assimilate surface observations only (ERA-20C, 20CRv2c, and 20CRv3), despite the latter being affected by a large negative temperature variance bias over South Africa.