Temperature and precipitation seasonal forecasts over the Mediterranean region: added value compared to simple forecasting methods

This study considers a set of state-of-the-art seasonal forecasting systems (ECMWF, MF, UKMO, CMCC, DWD and the corresponding multi-model ensemble) and quantifies their added value (if any) in predicting seasonal and monthly temperature and precipitation anomalies over the Mediterranean region compared to a simple forecasting method based on the ERA5 climatology (CTRL) or the persistence of the ERA5 anomaly (PERS). This analysis considers two starting dates, May 1st and November 1st and the forecasts at lead times up to 6 months for each year in the period 1993–2014. Both deterministic and probabilistic metrics are employed to derive comprehensive information on the forecast quality in terms of association, reliability/resolution, discrimination, accuracy and sharpness. We find that temperature anomalies are better reproduced than precipitation anomalies with varying spatial patterns across different forecast systems. The Multi-Model Ensemble (MME) shows the best agreement in terms of anomaly correlation with ERA5 precipitation, while PERS provides the best results in terms of anomaly correlation with ERA5 temperature. Individual forecast systems and MME outperform CTRL in terms of accuracy of tercile-based forecasts up to lead time 5 months and in terms of discrimination up to lead time 2 months. All seasonal forecast systems also outperform elementary forecasts based on persistence in terms of accuracy and sharpness

Empowering Early Career Polar Researchers in a changing climate: Challenges and solutions

Climate change is rapidly reshaping the research landscape in the polar regions. As such, Early Career Researchers (ECRs) face increasingly daunting challenges. These challenges include international and institutional competition for funding, shifting research demands and priorities, limited data sharing, and the need for strong mentorship

On the Radiative Impact of Biomass-Burning Aerosols in the Arctic: The August 2017 Case Study

Boreal fires have increased during the last years and are projected to become more intense and frequent as a consequence of climate change. Wildfires produce a wide range of effects on the Arctic climate and ecosystem, and understanding these effects is crucial for predicting the future evolution of the Arctic region. This study focuses on the impact of the long-range transport of biomass-burning aerosol into the atmosphere and the corresponding radiative perturbation in the shortwave frequency range. As a case study, we investigate an intense biomass-burning (BB) event which took place in summer 2017 in Canada and subsequent northeastward transport of gases and particles in the plume leading to exceptionally high values (0.86) of Aerosol Optical Depth (AOD) at 500 nm measured in northwestern Greenland on 21 August 2017. This work characterizes the BB plume measured at the Thule High Arctic Atmospheric Observatory (THAAO; 76.53° N, °68.74° W) in August 2017 by assessing the associated shortwave aerosol direct radiative impact over the THAAO and extending this evaluation over the broader region (60° N-80° N, 110° W-0° E). The radiative transfer simulations with MODTRAN6.0 estimated an aerosol heating rate of up to 0.5 K/day in the upper aerosol layer (8-12 km). The direct aerosol radiative effect (ARE) vertical profile shows a maximum negative value of -45.4 Wm-2 for a 78° solar zenith angle above THAAO at 3 km altitude. A cumulative surface ARE of -127.5 TW is estimated to have occurred on 21 August 2017 over a portion (3.1 10^6 km2) of the considered domain (60° N-80° N, 110° W-0° E). ARE regional mean daily values over the same portion of the domain vary between -65 and -25 Wm-2. Although this is a limited temporal event, this effect can have significant influence on the Arctic radiative budget, especially in the anticipated scenario of increasing wildfires

Algae drive enhanced darkening of bare ice on the Greenland ice sheet

Surface ablation of the Greenland ice sheet is amplified by surface darkening caused by light-absorbing impurities such as mineral dust, black carbon, and pigmented microbial cells. We present the first quantitative assessment of the microbial contribution to the ice sheet surface darkening, based on field measurements of surface reflectance and concentrations of light-absorbing impurities, including pigmented algae, during the 2014 melt season in the southwestern part of the ice sheet. The impact of algae on bare ice darkening in the study area was greater than that of non-algal impurities and yielded a net albedo reduction of 0.038 ± 0.0035 for each algal population doubling. We argue that algal growth is a crucial control of bare ice darkening, and incorporating the algal darkening effect will improve mass balance and sea level projections of the Greenland ice sheet and ice masses elsewhere

Accelerating Climate Action: A just transition in a post-Covid era. Book of abstracts, 9th SISC Annual Conference (online, 22-24 Set 2021)

Interest in seasonal forecasts has been increasing due to their potential applications in different economic and socially relevant sectors, including water management, agriculture and energy production. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly and seasonal time scale. The focus area is the Mediterranean, a densely populated region where seasonal forecasts could be helpful in a variety of economic sectors, including water management, hydropower production and agriculture. In this analysis, seasonal forecast systems issued by 5 European institutions (ECMWF, MétéoFrance, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them and a persistence (PERS) forecast, have been analysed. The added value of these forecast systems with respect to the simpler forecast approach based on climatology has been investigated. Interest in seasonal forecasts has been increasing due to their potential applications in different economic and socially relevant sectors, including water management, agriculture and energy production. This study provides an overall assessment of the skills of the main seasonal forecast systems available in the Copernicus Climate Data Store (C3S) in representing temperature and precipitation anomalies at the monthly and seasonal time scale. The focus area is the Mediterranean, a densely populated region and a climatic hotspot. In this analysis, seasonal forecast systems issued by 5 European institutions (ECMWF, MétéoFrance, UKMO, DWD, CMCC), together with two different Multi-Model Ensembles (MME) derived from them and a persistence (PERS) forecast, have been analysed. The added value of these forecast systems with respect to the simpler forecast approach based on climatology has been investigated. Different deterministic (Anomaly Correlation Coefficient) and probabilistic scores (Brier Score, Fair Continuous Ranked Probability Score and Receiver Operating Characteristic Curve) have been employed to obtain an overall assessment of the “quality” of the forecasts (Wilks, 2011; Murphy, 1993; WMO, 2018), using ERA5 dataset as a reference. The ensemble quality is assessed through the discussion of rank histograms. We performed the analysis using 6-month forecasts starting on May 1st and November 1st to reproduce the following summer and the winter seasons, respectively, considering the forecasts at both monthly and seasonal time scales. To this purpose, a thorough analysis has been performed to quantify the effect of the data aggregation. In general, temperature anomalies are better reproduced than precipitation anomalies. As shown in rank histograms, the overall ensemble quality is better for temperature, especially during the winter months. After the first month (lead time 0), decreasing skills are evident for almost any skill score, variable, starting date and model. The persistence forecast shows low accuracy and sharpness. Since forecast skills vary in space and time across different models, forecast skills for specific domains should be considered before developing particular applications. Moreover, we recommend using an ensemble of models, such as the MME forecast. References Murphy, A.H. (1993), “What is a good forecast? An essay on the nature of goodness in weather forecasting”, Weather & Forecasting, 8(2), 281–293. WMO (2018), Guidance on Verification of Operational Seasonal Climate Forecasts, Issue WMO-1220. 81 pp. Wilks, D.S. (2011), Statistical Methods in the Atmospheric Sciences, Academic Press Inc

Seasonal prediction of mountain snow resources: an application in the Alps

The development of seasonal projections of the state of snow resources in the Alps is of particular interest for the management of water resources and tourism. We present the progress in the development of a modelling chain based on the seasonal forecast variables produced by seasonal prediction systems of the Copernicus Climate Change Service (C3S). Seasonal forecast variables of precipitation, near-surface air temperature, radiative fluxes, wind and humidity are downscaled at three selected instrumented sites, close to five Alpine glaciers, in the North-Western Italian Alps, eventually bias-corrected and finally used as input for a physically-based multi-layer snowpack model (Snowpack; Lehning et al. 2012). A stochastic downscaling procedure is used for precipitation data in order to allow an estimate of uncertainties linked to small-scale variability in the forcing. We evaluate uncertainties affecting the skill of the modelling chain in predicting the evolution of the winter snowpack in hindcast simulations, comparing against historical data of snow depth and snow water equivalent by automatic stations in the study areas. The chain is tested considering seasonal forecast starting dates of November 1st, which are relevant for the snowpack processes. The sensitivity of the snow model to the accuracy of the input variables is discussed

Upward Longwave Irradiance at the Thule High Arctic Atmospheric Observatory (THAAO_ULI)

Upward Longwave Irradiance at the Thule High Arctic Atmospheric Observatory (THAAO_ULI

Liquid Water Path

Liquid Water Pat