Improving Arctic Weather and Seasonal Climate Prediction: Recommendations for Future Forecast Systems Evolution from the European Project APPLICATE

Ortega, Pablo; Blockley, Edward W.; Køltzow, Morten; Massonnet, François; Sandu, Irina; Svensson, Gunilla; Navarro, Juan Camilo Acosta; Arduini, Gabriele; Batté, Lauriane; Bazile, Éric; Cruz-García, Rubén; Day, Jonathan J.; Fichefet, Thierry; Flocco, Daniela; Gupta, Mukesh Kumar; Hartung, Kerstin; Hawkins, Ed; Hinrichs, Claudia; Magnusson, Linus; Moreno-Chamarro, Eduardo; Pérez-Montero, Sergio; Ponsoni, Leandro; Semmler, Tido; Smith, Doug; Sterlin, Jean; Tjernström, Michael; Välisuo, Ilona; Jung, Thomas

doi:10.1175/bams-d-22-0083.1

Cited by 6 publications

(3 citation statements)

References 49 publications

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“…The substantial air‐temperature STDE improvement with NEW in northern land areas of North America and Eurasia is certainly one of the most striking feature of these global difference maps. These improvements can be attributed to the more advanced specification of land‐surface conditions with NEW, namely to better initial conditions caused by an increased number of observations used to initialize soil moisture and surface temperature in northern areas (associated with relatively frequent passes of the orbiting satellites compared with a sparse surface observation network), and to more realistic surface modelling, in agreement with previous studies (e.g., Køltzow et al ., 2019; Ortega et al ., 2022). Overall statistics informing on NEW versus OP performance for each continent will be presented later with a summary table.…”

Section: Resultsmentioning

confidence: 99%

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Bélair

Alavi

Carrera

et al. 2023

Quart J Royal Meteoro Soc

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In this study screen‐level analyses of air temperature and humidity are used to objectively evaluate the impact of a new land‐surface package being considered for implementation in Environment and Climate Change Canada (ECCC)'s medium‐range global deterministic numerical weather prediction (NWP) system. Through its control of heat, moisture, and momentum fluxes to the atmosphere, the land surface has a substantial impact on near‐surface meteorology and on the atmospheric boundary layer. The approach examined in this study is based on the comparison between model forecasts and screen‐level analyses. It demonstrates the impact on medium‐range NWP of a new land‐surface package that includes (i) a new set of databases to specify soils and land‐cover characteristics, (ii) improved land‐surface initial conditions obtained by the assimilation of space‐based remote‐sensing observations, and (iii) a more sophisticated scheme for land‐surface modelling. The evaluation method is shown to provide useful information on the impact of the new land‐surface package, including lead‐time‐averaged difference maps as well as plots showing the evolution with lead time of the standard deviation of errors (STDE) and of the temporal correlation between forecasts and analyses. The new land‐surface package has a positive impact on near‐surface forecasts of air temperature and humidity for a summertime period, with smaller STDE and larger temporal correlation for both variables. The improvement is greater for humidity than for air temperature. The maximum impact is found around seven‐day lead time, with substantial gains in absolute and relative values for STDE and temporal correlation. The positive impact is also quantified in terms of prediction hours, with gains of about one day at the medium range. Details of the pros and cons for this objective evaluation approach are discussed.

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Section: Resultsmentioning

confidence: 99%

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Bélair

Alavi

Carrera

et al. 2023

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

show abstract

“…Indeed, several studies with coupled forecast models and more complex surface roughness treatments have shown improvements in near-surface wind speeds (Elvidge et al, 2023;Renfrew et al, 2019;Schneider et al, 2022;Thomas et al, 2021). Other studies have highlighted the importance of improving parameterizations including form drag for improving forecasting in the Arctic (Ortega et al, 2022;Ponsoni et al, 2021). Therefore, employing more sophisticated surface roughness parameterizations in state-of-the-art coupled earth system models that run for 10-100 s of years in different ice and ocean conditions could impact climate projections of near-surface winds.…”

Section: Discussionmentioning

confidence: 99%

Investigating Future Arctic Sea Ice Loss and Near‐Surface Wind Speed Changes Related to Surface Roughness Using the Community Earth System Model

DuVivier,

Vavrus,

Holland

et al. 2023

JGR Atmospheres

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The Arctic is undergoing a pronounced and rapid transformation in response to changing greenhouse gasses, including reduction in sea ice extent and thickness. There are also projected increases in near‐surface Arctic wind. This study addresses how the winds trends may be driven by changing surface roughness and/or stability in different Arctic regions and seasons, something that has not yet been thoroughly investigated. We analyze 50 experiments from the Community Earth System Model version 2 (CESM2) Large Ensemble and five experiments using CESM2 with an artificially decreased sea ice roughness to match that of the open ocean. We find that with a smoother surface there are higher mean wind speeds and slower mean ice speeds in the Autumn, Winter, and Spring. The artificially reduced surface roughness also strongly impacts the wind speed trends in Autumn and Winter, and we find that atmospheric stability changes are also important contributors to driving wind trends in both experiments. In contrast to the clear impacts on winds, the sea ice mean state and trends are statistically indistinguishable, suggesting that near‐surface winds are not major drivers of Arctic sea ice loss. Two major results of this work are: 1) the near‐surface wind trends are driven by changes in both surface roughness and near‐surface atmospheric stability that are themselves changing from sea ice loss, and 2) the sea ice mean state and trends are driven by the overall warming trend due to increasing greenhouse gas emissions and not significantly impacted by coupled feedbacks with the surface winds.

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“…The reasons for the lower predictive skills in the Arctic are various and often linked to the particularities of the Arctic climate system. One obvious issue in the Arctic results from the sparse observational coverage, which limits the data assimilation (Bauer et al, 2016;Jung and Matsueda, 2016;Lawrence et al, 2019;Ortega et al, 2022). Furthermore, the modeling of the sea ice cover is a major obstacle for correctly representing the Arctic surface energy budget but is still uncertain due to the complexity of sea ice dynamics (Day et al, 2022).…”

mentioning

confidence: 99%

Evaluation of downward and upward solar irradiances simulated by the Integrated Forecasting System of ECMWF using airborne observations above Arctic low-level clouds

Müller,

Ehrlich,

Jäkel

et al. 2023

Preprint

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Abstract. The simulations of upward and downward irradiances by the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts are compared to broadband solar irradiance measurements from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign. For this purpose, offline radiative transfer simulations with the ecRad radiation scheme using the operational IFS output were performed. The simulations of the downward solar irradiance agree within the measurement uncertainty. However, the IFS underestimates the reflected solar irradiances above sea ice significantly by −35 Wm−2. Above open ocean, the agreement is closer with an overestimation of 29 Wm−2. A sensitivity study using measured surface and cloud properties is performed with ecRad to quantify the contributions of the surface albedo, cloud fraction, ice and liquid water path and cloud droplet number concentration to the observed bias. It shows that the IFS sea ice albedo climatology underestimates the observed sea ice albedo, causing more than 50 % of the bias. Considering the higher variability of in situ observations in the parameterization of the cloud droplet number concentration leads to a smaller bias of −27 Wm−2 above sea ice and a larger bias of 48 Wm−2 above open ocean by increasing the range from 36–69 cm−3 to 36–200 cm−3. Above sea ice, realistic surface albedos, cloud droplet number concentrations and liquid water paths contribute most to a bias improvement. Above open ocean, realistic cloud fractions and liquid water paths are most important to reduce the model-observation differences.

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Improving Arctic Weather and Seasonal Climate Prediction: Recommendations for Future Forecast Systems Evolution from the European Project APPLICATE

Cited by 6 publications

References 49 publications

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Evaluating the impact of land surface on medium‐range weather forecasts using screen‐level analyses

Investigating Future Arctic Sea Ice Loss and Near‐Surface Wind Speed Changes Related to Surface Roughness Using the Community Earth System Model

Evaluation of downward and upward solar irradiances simulated by the Integrated Forecasting System of ECMWF using airborne observations above Arctic low-level clouds

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