Didier Monselesan joined CSIRO Marine and Atmospheric Research, in 2008, where he is participating in the Centre for Australian Weather and Climate Research sea level studies and forecasting efforts. Didier started his Australian career at the Australian Antarctic Division (AAD) in the Upper Atmosphere Physics group as an expeditioner wintering at Casey Station in 1993 and 1995. He pursued his interests in upper atmospheric physics at the Ionospheric Prediction Services (IPS) Radio and Space services in Sydney. His focus gradually shifted down from the upper atmosphere to mesospheric and stratospheric studies when rejoining the Australian National Antarctic Research Expedition to work on the AAD LIDAR experiment at Davis Station from 2006 to 2008. On his return, he decided to take a plunge into the Ocean by joining the CSIRO Marine and Atmospheric Research laboratories in Hobart.
El Nino-Southern Oscillation global (ENSO) imprint on sea surface temperature comes in many guise... more El Nino-Southern Oscillation global (ENSO) imprint on sea surface temperature comes in many guises. To identify its tropical fingerprints and impacts on the rest of the climate system, we propose a global approach based on archetypal analysis (AA), a pattern recognition method based on the identification of extreme configurations in the dataset under investigation. Relying on detrended sea surface temperature monthly anomalies over the 1982 to 2022 period, the technique recovers central and eastern Pacific ENSO types identified by more traditional methods and allows one to hierarchically add extra flavours and nuances to both persistent and transient phases of the phenomenon. Archetypal patterns found compare favorably to phase identification from K-means, fuzzy C-means and recently published network-based machine-learning algorithms. The AA implementation is modified for the identification of ENSO phases in sub-seasonal-toseasonal prediction systems and complements current alert systems in characterising the diversity of ENSO and its teleconnections. Tropical and extra-tropical teleconnection composites from various oceanic and atmospheric fields derived from the analysis are shown to be robust and physically relevant. Extending AA to sub-surface ocean fields improves the discrimination between phases when the characterisation of ENSO based on sea surface temperature is uncertain. We show that AA on detrended sea-level monthly anomalies provides a clearer expression of ENSO types.
Australian Antarctic Division lidar scientist, Didier Monselesan, detected the first Polar Strato... more Australian Antarctic Division lidar scientist, Didier Monselesan, detected the first Polar Stratospheric Clouds (PSC) over Davis, Antarctica, in May 2007. They occur only at high polar latitudes in winter, when temperatures in the stratosphere fall below about -85 degrees C and cause chemical changes that lead to the depletion of the ozone layer over Antarctica.
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The record-shattering hot day in the Pacific Northwest in June 2021 is used to motivate a study o... more The record-shattering hot day in the Pacific Northwest in June 2021 is used to motivate a study of record-shattering temperature extremes in a very large hindcast ensemble. The hottest days in the Pacific Northwest in the large ensemble have similar large scale and synoptic patterns to those associated with the observed event. From the perspective of a fixed location, the hottest ensemble days are acutely sensitive to the chance sequencing of a dry period with a precisely positioned weather pattern. These days are thus rare and require very large samples (tens of thousands of years) to capture. The enduring nature of record-shattering heat records can be understood through this lens of weather ‘noise’ and sampling. When a record-shattering event occurs due to chance alignment of weather systems in the optimal configuration, any small sample of years subsequent to the (very unlikely) record event has an extremely low chance of finding yet another chance extreme. While warming of the baseline climate can narrow the gap between more regular extremes and record-shattering extremes, this can take many decades depending on the pace of climate change. Climate models are unlikely to capture record-shattering extremes at fixed locations given by observations unless the model samples are large enough to provide enough weather outcomes to include the optimal weather alignments. This underscores the need to account for sampling in assessing models and changes in weather-sensitive extremes. In particular, climate models are not necessarily deficient in representing extremes if that assessment is based on their absence in undersize samples.
Between June 2019 and March 2020, thousands of wildfires spread devastation across Australia at t... more Between June 2019 and March 2020, thousands of wildfires spread devastation across Australia at the tragic cost of many lives, vast areas of burnt forest, and estimated economic losses upward of AU$100 billion. Exceptionally hot and dry weather conditions, and preceding years of severe drought across Australia, contributed to the severity of the wildfires. Here we present analysis of a very large ensemble of initialized climate simulations to assess the likelihood of the concurrent drought and fire-weather conditions experienced at that time. We focus on a large region in southeast Australia where these fires were most widespread and define two indices to quantify the susceptibility to fire from drought and fire weather. Both indices were unprecedented in the observed record in 2019. We find that the likelihood of experiencing such extreme susceptibility to fire in the current climate was 0.5%, equivalent to a 200 year return period. The conditional probability is many times higher ...
<p&amp... more <p>Subseasonal prediction skill of precipitation is typically low. Sometimes, however, forecasts are accurate and it would be useful to end-users to assess <em>a priori</em> if this might be the case. We use a 20-year hindcast data set of the ECMWF S2S prediction system and identify periods of high forecast confidence, evaluating model skill of precipitation forecasts for these periods compared to lower confidence predictions.</p><p>From reanalysis data, we derive a set of circulation patterns, called archetypes, that represent the broad-scale atmospheric circulation over Australia. These archetypes are combinations of ridges and troughs, and yield different precipitation patterns depending on the location of these features. In the literature, a typical application of circulation patterns is assigning daily reanalysis fields to the closest-matching pattern, thus obtaining conditional distributions of precipitation corresponding to key modes of atmospheric variability. A problem common to such analyses is that the precipitation distributions associated with the circulation patterns can be too similar; distinct distributions are required in order for the patterns to be useful in estimating precipitation. We show that by subsampling the archetype occurrences only when they are particularly well-matched to the underlying field, the conditional precipitation distributions become more distinct.</p><p>We subsample hindcast fields in the same way, obtaining a sample of periods when the model is confident about its prediction of the upcoming archetype. We then calculate model skill in predicting precipitation for three regions in southern Australia during such periods compared to when the model is not confident about the predicted archetype. Our results suggest that during periods of forecast confidence, precipitation skill is greater than normal for shorter leads (up to ten days) in two of the three regions (the Murray Basin and Western Tasmania). Skill for the third region (Southwest Western Australia) is greater during confident periods for lead times greater than one week, although this is marginal.</p>
The Southern Ocean has taken up more than 40% of the total anthropogenic carbon (Cant) stored in ... more The Southern Ocean has taken up more than 40% of the total anthropogenic carbon (Cant) stored in the oceans since the preindustrial era, mainly in subantarctic mode and intermediate waters (SAMW-AAIW). However, the physical mechanisms responsible for the transfer of Cant into the ocean interior remain poorly understood. Here, we use high resolution (1/10°) ocean simulations to investigate these mechanisms at the SAMW-AAIW subduction hotspots. Mesoscale Stationary Rossby Waves (SRWs), generated where the Antarctic Circumpolar Current interacts with topography, make the dominant contribution to the Cant transfer in SAMW-AAIW in the Indian and Pacific sectors (66% and 95% respectively). Eddy-resolving simulations reproduce the observed Cant sequestration in these layers, while lower spatial resolution models, that do not reproduce SRWs, underestimate the inventory of Cant in these layers by 40% and overestimate the storage in denser layers. A key implication is that climate model simul...
El Nino-Southern Oscillation global (ENSO) imprint on sea surface temperature comes in many guise... more El Nino-Southern Oscillation global (ENSO) imprint on sea surface temperature comes in many guises. To identify its tropical fingerprints and impacts on the rest of the climate system, we propose a global approach based on archetypal analysis (AA), a pattern recognition method based on the identification of extreme configurations in the dataset under investigation. Relying on detrended sea surface temperature monthly anomalies over the 1982 to 2022 period, the technique recovers central and eastern Pacific ENSO types identified by more traditional methods and allows one to hierarchically add extra flavours and nuances to both persistent and transient phases of the phenomenon. Archetypal patterns found compare favorably to phase identification from K-means, fuzzy C-means and recently published network-based machine-learning algorithms. The AA implementation is modified for the identification of ENSO phases in sub-seasonal-toseasonal prediction systems and complements current alert systems in characterising the diversity of ENSO and its teleconnections. Tropical and extra-tropical teleconnection composites from various oceanic and atmospheric fields derived from the analysis are shown to be robust and physically relevant. Extending AA to sub-surface ocean fields improves the discrimination between phases when the characterisation of ENSO based on sea surface temperature is uncertain. We show that AA on detrended sea-level monthly anomalies provides a clearer expression of ENSO types.
Australian Antarctic Division lidar scientist, Didier Monselesan, detected the first Polar Strato... more Australian Antarctic Division lidar scientist, Didier Monselesan, detected the first Polar Stratospheric Clouds (PSC) over Davis, Antarctica, in May 2007. They occur only at high polar latitudes in winter, when temperatures in the stratosphere fall below about -85 degrees C and cause chemical changes that lead to the depletion of the ozone layer over Antarctica.
&... more &am…
The record-shattering hot day in the Pacific Northwest in June 2021 is used to motivate a study o... more The record-shattering hot day in the Pacific Northwest in June 2021 is used to motivate a study of record-shattering temperature extremes in a very large hindcast ensemble. The hottest days in the Pacific Northwest in the large ensemble have similar large scale and synoptic patterns to those associated with the observed event. From the perspective of a fixed location, the hottest ensemble days are acutely sensitive to the chance sequencing of a dry period with a precisely positioned weather pattern. These days are thus rare and require very large samples (tens of thousands of years) to capture. The enduring nature of record-shattering heat records can be understood through this lens of weather ‘noise’ and sampling. When a record-shattering event occurs due to chance alignment of weather systems in the optimal configuration, any small sample of years subsequent to the (very unlikely) record event has an extremely low chance of finding yet another chance extreme. While warming of the baseline climate can narrow the gap between more regular extremes and record-shattering extremes, this can take many decades depending on the pace of climate change. Climate models are unlikely to capture record-shattering extremes at fixed locations given by observations unless the model samples are large enough to provide enough weather outcomes to include the optimal weather alignments. This underscores the need to account for sampling in assessing models and changes in weather-sensitive extremes. In particular, climate models are not necessarily deficient in representing extremes if that assessment is based on their absence in undersize samples.
Between June 2019 and March 2020, thousands of wildfires spread devastation across Australia at t... more Between June 2019 and March 2020, thousands of wildfires spread devastation across Australia at the tragic cost of many lives, vast areas of burnt forest, and estimated economic losses upward of AU$100 billion. Exceptionally hot and dry weather conditions, and preceding years of severe drought across Australia, contributed to the severity of the wildfires. Here we present analysis of a very large ensemble of initialized climate simulations to assess the likelihood of the concurrent drought and fire-weather conditions experienced at that time. We focus on a large region in southeast Australia where these fires were most widespread and define two indices to quantify the susceptibility to fire from drought and fire weather. Both indices were unprecedented in the observed record in 2019. We find that the likelihood of experiencing such extreme susceptibility to fire in the current climate was 0.5%, equivalent to a 200 year return period. The conditional probability is many times higher ...
<p&amp... more <p>Subseasonal prediction skill of precipitation is typically low. Sometimes, however, forecasts are accurate and it would be useful to end-users to assess <em>a priori</em> if this might be the case. We use a 20-year hindcast data set of the ECMWF S2S prediction system and identify periods of high forecast confidence, evaluating model skill of precipitation forecasts for these periods compared to lower confidence predictions.</p><p>From reanalysis data, we derive a set of circulation patterns, called archetypes, that represent the broad-scale atmospheric circulation over Australia. These archetypes are combinations of ridges and troughs, and yield different precipitation patterns depending on the location of these features. In the literature, a typical application of circulation patterns is assigning daily reanalysis fields to the closest-matching pattern, thus obtaining conditional distributions of precipitation corresponding to key modes of atmospheric variability. A problem common to such analyses is that the precipitation distributions associated with the circulation patterns can be too similar; distinct distributions are required in order for the patterns to be useful in estimating precipitation. We show that by subsampling the archetype occurrences only when they are particularly well-matched to the underlying field, the conditional precipitation distributions become more distinct.</p><p>We subsample hindcast fields in the same way, obtaining a sample of periods when the model is confident about its prediction of the upcoming archetype. We then calculate model skill in predicting precipitation for three regions in southern Australia during such periods compared to when the model is not confident about the predicted archetype. Our results suggest that during periods of forecast confidence, precipitation skill is greater than normal for shorter leads (up to ten days) in two of the three regions (the Murray Basin and Western Tasmania). Skill for the third region (Southwest Western Australia) is greater during confident periods for lead times greater than one week, although this is marginal.</p>
The Southern Ocean has taken up more than 40% of the total anthropogenic carbon (Cant) stored in ... more The Southern Ocean has taken up more than 40% of the total anthropogenic carbon (Cant) stored in the oceans since the preindustrial era, mainly in subantarctic mode and intermediate waters (SAMW-AAIW). However, the physical mechanisms responsible for the transfer of Cant into the ocean interior remain poorly understood. Here, we use high resolution (1/10°) ocean simulations to investigate these mechanisms at the SAMW-AAIW subduction hotspots. Mesoscale Stationary Rossby Waves (SRWs), generated where the Antarctic Circumpolar Current interacts with topography, make the dominant contribution to the Cant transfer in SAMW-AAIW in the Indian and Pacific sectors (66% and 95% respectively). Eddy-resolving simulations reproduce the observed Cant sequestration in these layers, while lower spatial resolution models, that do not reproduce SRWs, underestimate the inventory of Cant in these layers by 40% and overestimate the storage in denser layers. A key implication is that climate model simul...
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Papers by Didier P Monselesan