Rapid Development of Systematic Errors in Seasonal Forecasts

Jonathan Beverley

NOAA Physical Sciences Laboratory - CIRES CU Boulder

Tuesday, Nov 19, 2024, 2:00 pm MT
DSRC Room GC402

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Abstract

It is known that systematic seasonal forecast errors are dominated by model errors that develop quickly, within a few months following initialisation. In this seminar, I will present results of analysis focusing on two of these errors.

In the first half of the talk, I will focus on errors relating to El Niño-Southern Oscillation (ENSO). We find that the eleven different operational forecast models analysed have a systematic westward SST anomaly bias, whereby the eastern-central tropical Pacific SST anomalies associated with ENSO events extend too far to the west for anomalies of either sign. Associated with this SST forecast error is a westward shift of ENSO rainfall anomalies, which in turn affects extratropical seasonal forecast skill through errors in wave propagation from the tropical Pacific. These ENSO-related forecast errors, which are also typical of long free-running climate model simulations, are apparent almost immediately; in fact, they develop so rapidly that they are primarily a function of the seasonal cycle, rather than lead time.

In the second half of the talk, I will show that many common climate model trend errors are also evident in seasonal hindcasts at short lead times over the 1993—2016 period. These hindcasts use models similar to CMIP-class models and include the same CMIP historical external forcings, but critically are initialised with observations, removing uncertainty related to internal variability. Notably, we find tropical Pacific "El Niño-like" SST trend errors in all seasons but boreal spring, and related errors in other variables. We suggest that these hindcast trend errors reflect the sensitivity of the model mean biases to the changing radiative forcing, rather than a forced response. That is, the similarity between errors in free running simulations and hindcasts is a result of the seasonal forecast models quickly transitioning from nature’s attractor to the climate model attractor, especially in the atmospheric model component.

Bio:

I received both my BSc in Meteorology and Climate and PhD in Atmosphere, Oceans and Climate in the Department of Meteorology at the University of Reading, UK. After that, I spent two and a half years as a postdoc at the University of Exeter, looking at future changes to ENSO teleconnections under climate change. I joined CIRES/PSL in February 2022 and since then have been working on diagnosing seasonal forecast errors.