Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region

Global Variability of Simulated and Observed Vegetation Growing Season

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The Sensitivity of Simulated River Discharge to Land Surface Representation and Meteorological Forcings

Abstract The discharge of freshwater into oceans represents a fundamental process in the global climate system, and this flux is taken into account in simulations with general circulation models (GCMs). Moreover, the availability of realistic river routing schemes is a powerful instrument to assess the validity of land surface components, which have been recognized to be crucial for the global climate simulation. In this study, surface and subsurface runoff generated by the 13 land surface schemes (LSSs) participating in the Second Global Soil Wetness Project (GSWP-2) are used as input fields for the Hydrology Discharge (HD) routing model to simulate discharge for 30 of the world's largest rivers. The simplest land surface models do not provide a good representation of runoff, and routed river flows using these inputs are affected by many biases. On the other hand, HD shows the best simulations when forced by two of the more sophisticated schemes. The multimodel ensemble GSWP-2 generates the best phasing of the annual cycle as well as a good representation of absolute values, although the ensemble mean tends to smooth the peaks. Finally, the intermodel comparison shows the limits and deficiencies of a velocity-constant routing model such as HD, particularly in the phase of mean annual discharge. The second part of the study assesses the sensitivity of river discharge to the variation of external meteorological forcing. The Center for Ocean–Land–Atmosphere Studies version of the SSiB model is constrained with different meteorological fields and the resulting runoff is used as input for HD. River flow is most sensitive to precipitation variability, but changes in radiative forcing affect discharge as well, presumably because of the interaction with evaporation. Also, this analysis provides an estimate of the sensitivity of river discharge to precipitation variations. A few areas (e.g., central and eastern Asia, the Mediterranean, and much of the United States) show a magnified response of river discharge to a given percentage change in precipitation. Hence, an amplified effect of droughts as indicated by the consensus of climate change predictions may occur in places such as the Mediterranean. Conversely, increasing summer precipitation foreseen in places like southern and eastern Asia may amplify floods in these poor and heavily populated regions. Globally, a 1% fluctuation in precipitation forcing results in an average 2.3% change in discharge. These results can be used for the definition and assessment of new strategies for land use and water management in the near future

Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model

Euro-Mediterranean Centre on Climate Change coupled climate model (CMCC-CM2) represents the new family of the global coupled climate models developed and used at CMCC. It is based on the atmospheric, land and sea ice components from the Community Earth System Model coupled with the global ocean model Nucleus for European Modeling of the Ocean. This study documents the model components, the coupling strategy, particularly for the oceanic, atmospheric, and sea ice components, and the overall model ability in reproducing the observed mean climate and main patterns of interannual variability. As a first step toward a more comprehensive, process-oriented, validation of the model, this work analyzes a 200-year simulation performed under constant forcing corresponding to present-day climate conditions. In terms of mean climate, the model is able to realistically reproduce the main patterns of temperature, precipitation, and winds. Specifically, we report improvements in the representation of the sea surface temperature with respect to the previous version of the model. In terms of mean atmospheric circulation features, we notice a realistic simulation of upper tropospheric winds and midtroposphere geopotential eddies. The oceanic heat transport and the Atlantic meridional overturning circulation satisfactorily compare with present-day observations and estimates from global ocean reanalyses. The sea ice patterns and associated seasonal variations are realistically reproduced in both hemispheres, with a better skill in winter. Main weaknesses of the simulated climate are related with the precipitation patterns, specifically in the tropical regions with large dry biases over the Amazon basin. Similarly, the seasonal precipitation associated with the monsoons, mostly over Asia, is weaker than observed. The main patterns of interannual variability in terms of dominant empirical orthogonal functions are faithfully reproduced, mostly in the Northern Hemisphere winter. In the tropics the main teleconnection patterns associated with El Nino-Southern Oscillation and with the Indian Ocean Dipole are also in good agreement with observations

Seasonal forecasts of the rainy season onset over Africa: Preliminary results from the FOCUS-Africa project

Precipitation seasonality is the main factor controlling vegetation phenology in many tropical and subtropical regions. Anticipating the rain onset is of paramount importance for field preparation and seeding. This is of particular importance in various African countries that rely on agriculture as a main source of food, subsistence and income. In such countries, skilful and accurate onset forecasts could also inform early warning and early actions, such as aids logistics planning, for food security. Here, we assess the skill of the seasonal forecast data provided by the Copernicus Climate Change Service in predicting the rain onset over Africa. The skill, i.e. the accuracy of the seasonal forecasts simulation ensemble compared to the climatology, is computed in a probabilistic fashion by accounting for the frequencies of normal, early and late onsets predicted by the forecast system. We compute the skill using the hindcasts (forecast simulations conducted for the past) starting at the beginning of each month in the period 1993–2016. We detect the onset timing of the rainy season using a non-parametric method that accounts for double seasonality and is suitable for the specific time-window of the seasonal forecast simulations. We find positive skills in some key African agricultural regions some months in advance. Overall, the multi-model ensemble outperforms any individual model ensemble. We provide targeted recommendations to develop a useful climate service for the agricultural sector in Africa

Education for innovation and entrepreneurship in the food system: the Erasmus+ BoostEdu approach and results

Innovation and entrepreneurship are key factors to provide added value for food systems. Based on the findings of the Erasmus+ Strategic Partnership BoostEdu, the objective of this paper is to provide answers to three knowledge gaps: 1) identify the needs for innovation and entrepreneurship (I&E) in the food sector; 2) understand the best way to organize learning; 3) provide flexibility in turbulent times. BoostEdu aimed to provide a platform for continuing education within I&E for food professionals and was carried out through co-creation workshops and the development of an e-learning course. The results of the project in particular during the Covid-19 pandemics, highlighted the need for flexible access to modules that are complementary to other sources and based on a mix of theoretical concepts and practical experiences. The main lessons learned concern the need of co-creation and co-learning processes to identify suitable practices for the use of innovative digital technologies

Cost-effectiveness of Lifestyle Africa: an adaptation of the diabetes prevention programme for delivery by community health workers in urban South Africa

Background Lifestyle Africa is an adapted version of the Diabetes Prevention Program designed for delivery by community health workers to socioeconomically disadvantaged populations in low- and middle-income countries (LMICs). Results from the Lifestyle Africa trial conducted in an under-resourced community in South Africa indicated that the programme had a significant effect on reducing haemoglobin A1c (HbA1c). Objective To estimate the cost of implementation and the cost-effectiveness (in cost per point reduction in HbA1c) of the Lifestyle Africa programme to inform decision-makers of the resources required and the value of this intervention. Methods Interviews were held with project administrators to identify the activities and resources required to implement the intervention. A direct-measure micro-costing approach was used to determine the number of units and unit cost for each resource. The incremental cost per one point improvement in HbA1c was calculated. Results The intervention equated to 71 United States dollars (USD) in implementation costs per participant and a 0.26 improvement in HbA1c per participant. Conclusions Lifestyle Africa reduced HbA1c for relatively little cost and holds promise for addressing chronic disease in LMIC. Decision-makers should consider the comparative clinical effectiveness and cost-effectiveness of this intervention when making resource allocation decisions

Innovation in Food and Agriculture

Innovation means change. In a case of food and agriculture, it can be the application of new proposals for raw material processing technology, packaging of products, new food additives, and new agricultural technologies. Innovation may lead to reducing or preventing adverse changes caused by microorganisms, oxidation of food ingredients, and enzymatic and nonenzymatic reactions, as well as ensuring safety by inhibiting the development of some pathogenic microorganisms. Change can also provide healthier and more nutritious food. The food is tastier because of the prevention of adverse qualitative changes in food composition, including organoleptic changes, and changes in the perception and pleasures from eating food. In addition, crops can be more abundant thanks to reduced exposure to diseases, adapted agricultural treatments, or higher resistance to changing weather conditions