Earth System Physics


Weather and Climate: From Fundamentals to Applications

An ICTP/UniTN/UniAQ Joint International Seminar Series


This series of lectures is a joint initiative between the Earth System Physics group at ICTP, the Atmospherics Physics group at the University of Trento, and Atmospheric Physics group at the University of L'Aquila. They present the current state of research and operational implementation of all areas of weather and climate, past, present and future, and span basic theory, idealized and realistic modelling, through to applications of climate and weather in sectors that include the health, agriculture, energy and the economy. These seminars are open to all those with an interest in climate and weather and are intended to be accessible to all levels from undergraduate students to senior scientists in the field.


Adrian Tompkins (ICTP) and Simona Bordoni (UniTN) and Rossella Ferretti (UniAQ)


Participants are required to register in advance at:
After registering, you will receive a confirmation email containing information about joining the meeting. All meetings are scheduled on Tuesdays at 15:30 UTC+1 (CET/Rome)

Seminar Summary

15/03/2022 15:30 @ UniTN
Icy, Moist, and Seasonal: Polar Amplification in Simple and Complex Climates

nicole feldl

Nicole Feldl
Assistant Professor at UCSC
Santa Cruz, U.S.A.

orcid logo


Since the 1970s, simulations of climate change forced by increased CO2 concentrations have predicted warming that is greatest in polar regions. This polar-amplified warming has been variously attributed to the ice-albedo feedback, associated with the retreat of reflective sea ice in summer; the lapse rate feedback, associated with vertically nonuniform atmospheric warming in winter; and changes in energy transport by atmospheric circulations. Uncertainty in projections of Arctic climate change arise in part from incomplete understanding of the interconnected nature of these processes. Here, I present a new implementation of an idealized model to systematically investigate the roles of moist energy transport and sea ice in driving polar amplification. I additionally introduce a diagnostic decomposition of the lapse rate feedback, applied to an ensemble of comprehensive models, that isolates influences from dynamically distinct atmospheric regions. These analyses reveal the importance of ice thermodynamics and how the lapse-rate and sea-ice albedo feedback together dominate Arctic amplification as a coupled ocean-atmosphere mechanism operating across the seasonal cycle.


22/03/2022 15:30 @ ICTP
Extreme Events in a Warmer World – Making the Impacts of Climate Change More Tangible

thomas jung

Thomas Jung
Professor at Alfred Wegener Institute
Bremerhaven, Germany

orcid logo


29/03/2022 15:30 @ UniAQ
How Good are We at Predicting Fires?


Francesca Di Giuseppe
Fire Forecasting Project Manager at the ECMWF
Reading, United Kingdom

orcid logo


The prediction of fire danger conditions allows fire management agencies to implement fire prevention, detection and pre-suppression action plans before fire damages occur. However, in many countries fire danger rating relies on observed weather data, which only allows for daily environmental monitoring of fire conditions. Even when this estimation is enhanced with the combined use of satellite data, such as hot spots for early fire detection and land cover and fuel conditions, it normally only provides 4 to 6 h warnings. By using forecast conditions from advanced numerical weather models, early warning could be extended by up to 1–2 weeks, allowing for greater coordination of resource-sharing and mobilization within and across countries.

Using 1 year of pre-operational service in 2017 and the Fire Weather Index (FWI), in this talk we assess the capability of the system to predict fire danger globally and analyse in detail three major events in Chile, Portugal and California. The analysis shows that the skill provided by an ensemble forecast system extends to more than 10 days when compared to the use of mean climate, making a case for extending the forecast range to the sub-seasonal to seasonal timescale. However, accurate FWI prediction does not translate into accuracy in the forecast of fire activity globally. Indeed, when all fires detected in 2017 are considered, including agricultural- and human-induced burning, high FWI values only occur in 50 % of the cases and are limited to the Boreal regions. Nevertheless, for very large events which were driven by weather conditions, FWI forecasts provide advance warning that could be instrumental in setting up management and containment strategies.


12/04/2022 15:30 @ ICTP
Serial Clustering of Extratropical Cyclones: Where, When and Why It Occurs

helen dacre

Helen Dacre
Professor at University of Reading
Reading, United Kingdom



26/04/2022 15:30 @ UniTN
Understanding the Processes that Drive Surface Temperature Variability


David Thompson
Professor at Colorado State University and University of East Anglia
Fort Collins, U.S.A. / Norwich, England

orcid logo


In this talk I will provide highlights of two different - but overlapping - research topics. Both relate to the processes that drive surface temperature variability.

In the first part I will focus on the role of ocean dynamics in driving sea-surface temperature variability. The key conclusion is that ocean dynamics act - in the net - to damp rather than drive low-frequency SST variability. The upshot is that the observed decadal variability - such as that associated with Atlantic decadal variability and the Pacific Decadal Oscillation - can not be traced to ocean processes.

In the second part I will focus on changes in temperature persistence under climate change. Here the key finding is that temperature persistence is certain to change across the globe, but that the signs of the changes depend critically on various physical processes. I will argue that one key but overlooked process is the changes in radiative damping timescales under climate change.


03/05/2022 15:30 @ ICTP
Exploring the Manifold of the Tropical Pacific in Observations and Models

annalisa bracco

Annalisa Bracco
Professor at Earth and Atmospheric Sciences, Georgia Institute of Technology
Atlanta, U.S.A.

orcid logo


The climate system is multiscale, multidimensional and nonlinear. Here we propose a robust framework for visualizing and analyzing its dynamics, accounting for both dependencies and nonlinearities. At each time t, the system is uniquely described by a state space vector parameterized by N variables and their spatial variability. The dynamics is confined on a manifold with dimension lower than the full state space and a strategy for manifold learning is presented via linear and nonlinear algorithms. We focus on the Tropical Pacific Ocean using a reanalysis as observational proxy (ERA5) and two state-of-the-art models from the CMIP6 catalog, MPI and EC-Earth3.

The analysis spans four variables over two 40 years periods at daily frequency, during historical times and in the SSP585 scenario. The manifold learning step allows for comparing nonlinear contributions as well as the relative role of each variable in the system's dynamics. Instantaneous properties of the high dimensional attractor are then quantified through the local dimension and persistence metrics, recently introduced to the climate community.

These metrics quantify geometrical properties of the manifold and the stability of local motions. Both models underestimate the average dimension and overestimate the potential predictability of Tropical Pacific climate compared to ERA5, which is indicative of common and persistent differences between modelled and observed dynamics. These model's biases are nearly identical during the historical period while diverging in the global warming scenario analyzed.


10/05/2022 15:30 @ UniTN
Viewing Anthropogenic Change Through an AI Lens


Elizabeth Barnes
Professor of Atmospheric Science at Colorado State University
Fort Collins, U.S.A.

orcid logo


Humans are vastly modifying the earth system, with identifiable impacts across the land surface, ocean and atmosphere. Here, we will explore three example applications of how explainable AI (XAI) techniques can help us visualize and quantify these changes over time. First, we will demonstrate how we can utilize XAI methods to quantify the footprint of human activity across the global land surface in near-real time. Second, we will demonstrate the utility of XAI for extracting forced climate patterns through time amidst a sea of climate noise and model disagreement. Third, we will show how XAI can help us better understand and predict temporal variations in decadal warming trends. All three parts of this talk serve as examples of how viewing our climate through an AI lens has the power to uncover new insights into anthropogenic change - allowing scientists to ask "why?" but now with the power of machine learning.


17/05/2022 15:30 @ UniAQ
Data Assimilation and Nowcasting for Air Traffic Management Purposes. Results from SINOPTICA Project


Vincenzo Mazzarella
Researcher at Fondazione CIMA (International Centre for Environmental Monitoring)
Savona, Italy

orcid logo


The number of short-term and highly localized phenomena, such as thunderstorms, hailstorms, wind gusts or tornadoes, is expected to grow further in the coming years, with important repercussions in air traffic management activities (ATM). One of the main challenges for meteorologists is to improve the location and timing of such events that develop on small spatial and temporal scales. In this regard, the H2020 Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM (SINOPTICA) project aims to demonstrate that numerical weather forecasts with high spatial and temporal resolution, benefiting from the assimilation of radar data, in situ weather stations, GNSS and lightning data, could improve the prediction of severe weather events for the benefit of air traffic control (ATC) and ATM.

To this purpose the output of the numerical simulations, carried out with the Weather Research and Forecasting (WRF) model using a cycling 3D-VAR assimilation technique, and the output of a nowcasting algorithm, will be integrated into the Arrival Manager 4D-CARMA (4-Dimensional Cooperative Arrival Manager), an adaptive air traffic sequencing and management system for controllers. 4D-CARMA upgrades 4D trajectories avoiding areas affected by adverse phenomena with the aims of increasing flight safety and reducing controllers' and pilots' workload.

The results prove that it is possible to improve the prediction of the above-mentioned events in line with expectations and ATM needs.


31/05/2022 15:30 @ UniTN
Impacts of Deep Convection on the Tropical Low Cloud Feedback and Climate Sensitivity


Kathleen Schiro
Assistant Professor at the University of Virginia
Charlottesville, U.S.A.

orcid logo


Climate model simulations are known to be sensitive to parameter choices in the sub grid-scale representation of deep convection, as deep convection plays a critical role in the transport of heat and momentum globally. Over the years, it has also become evident that the intermodel spread in the warming response to anthropogenic forcing is largely driven by uncertainties in the magnitude of the cloud feedback in the tropics, specifically the low cloud feedback. In this talk, I will discuss how parameterization differences among models and changes to deep convection in response to anthropogenic warming are likely contributing significantly to the intermodel spread in the tropical cloud feedback. I will present evidence of two physical pathways linking deep convection to low clouds and their response to anthropogenic forcing: the "Radiation-Subsidence" Pathway and the "Stability" Pathway. In a warmer world, the tropical overturning circulation is projected to weaken. We find that the overturning circulation does not weaken as much in climate models with more stable tropospheres, which ultimately leads to a more positive low cloud feedback (Stability Pathway). Differences in deep convective parameterization modifying deep convection onset thresholds – such as the fractional rate of entrainment into convective updrafts – can contribute significantly to this intermodel spread in static stability. Additionally, changes to the total area occupied by deep convection in the tropics modify the high cloud fraction, which is linked to subsidence changes and the low cloud feedback (Radiation-Subsidence Pathway). Results from both the Coupled Model Intercomparison Project (CMIP6) and a perturbation physics ensemble in the Community Earth System model (NCAR CESM) will be presented and discussed.


14/06/2022 15:30 @ ICTP
How Do Ocean Temperature Anomalies Favor or Disfavor the Aggregation of Deep Convective Clouds?

caroline muller

Caroline Muller
Assistant Professor at the Institute of Science and Technology (IST)
Klosterneuburg, Austria

orcid logo


Convective organization at mesoscales (hundreds of kilometres) is ubiquitous in the tropics, but the physical processes behind it are still poorly understood. Organization can be forced by the large scales, such as surface temperature gradients. But convective organization can also arise from internal feedbacks, such as "self-aggregation" feedbacks. Self-aggregation refers to the spectacular ability of deep clouds to spontaneously cluster in space despite spatially homogeneous conditions and no large-scale forcing, in high-resolution cloud-resolving models (CRMs).Because of the idealized settings in which self-aggregation has been studied (typically radiative-convective equilibrium (RCE) over homogeneous sea-surface temperature (SST)), its relevance to the real tropics is debated. In this presentation, we will investigate the impact of removing some of these idealizations on the aggregation process. Specifically, we will investigate the impact of inhomogeneous SSTs on convective aggregation.

In a first step, we will investigate how an idealized warm circular SST anomaly, referred to as "hot-spot", helps organize convection, and how self-aggregation feedbacks modulate this organization. The presence of a hot-spot significantly accelerates aggregation, particularly for larger domains and warmer/larger hot-spots, and extends the range of SSTs for which aggregation occurs. In that case, the aggregation onset results from a large-scale circulation induced by the hot-spot. In a second step, we will investigate the interaction of aggregation with an interactive surface (local SST evolving according to the surface energy budget). The results will be interpreted in light of a simple model for the boundary layer circulation.