ORCID Profile
0000-0002-2294-7823
Current Organisation
University of Aveiro
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: American Meteorological Society
Date: 10-2020
Abstract: The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) had a special observing period (SOP) that ran from 16 November 2018 to 15 February 2019, a period chosen to span the austral warm season months of greatest operational activity in the Antarctic. Some 2,200 additional radiosondes were launched during the 3-month SOP, roughly doubling the routine program, and the network of drifting buoys in the Southern Ocean was enhanced. An evaluation of global model forecasts during the SOP and using its data has confirmed that extratropical Southern Hemisphere forecast skill lags behind that in the Northern Hemisphere with the contrast being greatest between the southern and northern polar regions. Reflecting the application of the SOP data, early results from observing system experiments show that the additional radiosondes yield the greatest forecast improvement for deep cyclones near the Antarctic coast. The SOP data have been applied to provide insights on an atmospheric river event during the YOPP-SH SOP that presented a challenging forecast and that impacted southern South America and the Antarctic Peninsula. YOPP-SH data have also been applied in determinations that seasonal predictions by coupled atmosphere–ocean–sea ice models struggle to capture the spatial and temporal characteristics of the Antarctic sea ice minimum. Education, outreach, and communication activities have supported the YOPP-SH SOP efforts. Based on the success of this Antarctic summer YOPP-SH SOP, a winter YOPP-SH SOP is being organized to support explorations of Antarctic atmospheric predictability in the austral cold season when the southern sea ice cover is rapidly expanding.
Publisher: Copernicus GmbH
Date: 21-04-2021
Publisher: Copernicus GmbH
Date: 18-11-2020
DOI: 10.5194/ESSD-12-2959-2020
Abstract: Abstract. Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular ex le is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages). We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked ex le using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), 0.5281/zenodo.3998463 (Iturbide et al., 2020).
Publisher: Copernicus GmbH
Date: 30-11-2021
Abstract: Abstract. The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity the fact that sea ice controls sea water salinity, d ens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean–atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.
Publisher: Copernicus GmbH
Date: 21-04-2021
DOI: 10.5194/ESD-2021-16
Abstract: Abstract. The Southern Ocean is a critical component of Earth’s climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the dynamic state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, ocean biogeochemistry and microbiology. This circumpolar cruise included visits to twelve remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter term variations, the geographic setting of environmental processes, and interactions between them over the duration of 90 days. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable, we applied a sparse Principal Component Analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. We identified temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and “hotspots” of interaction. Our results establish connections of oceanic, atmospheric, biological and terrestrial processes in an innovative way, while confirming many well known relations of the Southern Ocean system. More specifically, we identify: the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity that sea ice predominantly controls sea water salinity, d ens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilization and reduced light limitation and clear regional patterns of aerosol characteristics emerged, stressing the role of the sea state, atmospheric chemical processing, as well as source processes near “hotspots” for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper ocean layer light intensity, surface wind speed and relative humidity, played an important role in the majority of latent variables, highlighting their importance for a large variety of processes and the necessity for Earth System Models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean-atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. The sPCA processing code is available as open-access and we believe that our approach is widely applicable to other environmental field studies.
Publisher: Copernicus GmbH
Date: 02-04-2020
Abstract: Abstract. Several sets of reference regions have been proposed in the literature for the regional synthesis of observed and model-projected climate change information. A popular ex le is the set of reference regions introduced in the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX) based on a prior coarser selection and then slightly modified for the 5th Assessment Report of the IPCC. This set was developed for reporting sub-continental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes (the typical resolution of the 5th Climate Model Intercomparison Projection, CMIP5, climate models was around 2º). These regions have been used as the basis for several popular spatially aggregated datasets, such as the seasonal mean temperature and precipitation in IPCC regions for CMIP5. Here we present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher model resolution (around 1º for CMIP6). As a result, the number of regions increased to 43 land plus 12 open ocean, better representing consistent regional climate features. The paper describes the rationale followed for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and shapefile together with companion R and Python notebooks to illustrate their use in practical problems (trimming data, etc.). We also describe the generation of a new dataset with monthly temperature and precipitation spatially aggregated in the new regions, currently for CMIP5 (for backwards consistency) and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked ex le using scatter diagrams to offer guidance on the likely range of future climate change at the scale of reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository github.com/SantanderMetGroup/ATLAS, doi:10.5281/zenodo.3688072 (Iturbide et al., 2020).
Location: France
No related grants have been discovered for Irina Gorodetskaya.