ALBERT

Description: Recent research has demonstrated that additional winter radiosonde observations in Arctic regions enhance the predictability of mid-latitude weather extremes by reducing uncertainty in the flow of localised tropopause polar vortices. The impacts of additional Arctic observations during summer are usually confined to high latitudes and they are difficult to realize at mid-latitudes because of the limited scale of localised tropopause polar vortices. However, in certain climatic states, the jet stream can intrude remarkably into the mid-latitudes, even in summer; thus, additional Arctic observations might improve analysis validity and forecast skill for summer atmospheric circulations over the Northern Hemisphere. This study examined such cases that occurred in 2016 by focusing on the prediction of the intensity and track of tropical cyclones (TCs) over the North Atlantic and North Pacific, because TCs are representative of extreme weather in summer. The predictabilities of three TCs were found influenced by additional Arctic observations. Comparisons with ensemble reanalysis data revealed that large errors propagate from the data-sparse Arctic into the mid-latitudes, together with high-potential-vorticity air. Ensemble forecast experiments with different reanalysis data confirmed that additional Arctic observations sometimes improve the initial conditions of upper-level troposphere circulations.

Description: Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that (1) have been utilized for improved model initialization, including concentration, thickness and drift, and (2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the “best” estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.

Description: A cloud particle sensor (CPS) sonde is an observing system attached with a radiosonde sensor to observe the vertical structure of cloud properties. The signals obtained from CPS sondes are related to the phase, size, and number of cloud particles. The system offers economic advantages including human resource and simple operation costs compared with aircraft measurements and land-/satellite-based remote sensing. However, the observed information should be appropriately corrected because of several uncertainties. Here we made field experiments in the Arctic region by launching approximately 40 CPS sondes between 2018 and 2020. Using these data sets, a better practical correction method was proposed to exclude unreliable data, estimate the effective cloud water droplet radius, and determine a correction factor for the total cloud particle count. We apply this method to data obtained in October 2019 over the Arctic Ocean and March 2020 at Ny-Ålesund, Svalbard, Norway, to compare with a particle counter aboard a tethered balloon and liquid water content retrieved by a microwave radiometer. The estimated total particle count and liquid water content from the CPS sondes generally agree with those data. Although further development and validation of CPS sondes based on dedicated laboratory experiments would be required, the practical correction approach proposed here would offer better advantages in retrieving quantitative information on the vertical distribution of cloud microphysics under the condition of a lower number concentration.

Description: The Year of Polar Prediction (YOPP) is planned for mid-2017 to mid-2019, centred on 2018. Its goal is to enable a significant improvement in environmental prediction capabilities for the polar regions and beyond, by coordinating a period of intensive observing, modelling, prediction, verification, user-engagement and education activities. With a focus on time scales from hours to a season, YOPP is a major initiative of the World Meteorological Organization’s World Weather Research Programme (WWRP) and a key component of the Polar Prediction Project (PPP). YOPP is being planned and coordinated by the PPP Steering Group together with representatives from partners and other initiatives, including the World Climate Research Programme’s Polar Climate Predictability Initiative (PCPI). The objectives of YOPP are to: 1. Improve the existing polar observing system (enhanced coverage, higher-quality observations). 2. Gather additional observations through field programmes aimed at improving understanding of key polar processes. 3. Develop improved representation of key polar processes in (un)coupled models used for prediction. 4. Develop improved (coupled) data assimilation systems accounting for challenges in the polar regions such as sparseness of observational data. 5. Explore the predictability of the atmosphere-cryosphere-ocean system, with a focus on sea ice, on time scales from hours to a season. 6. Improve understanding of linkages between polar regions and lower latitudes, assess skill of models representing these linkages, and determine the impact of improved polar prediction on forecast skill in lower latitudes. 7. Improve verification of polar weather and environmental predictions to obtain better quantitative knowledge on model performance, and on the skill, especially for user- relevant parameters. 8. Identify various stakeholders and establish their decisionmaking needs with respect to weather, climate, ice, and related environmental services. 9. Assess the costs and benefits of using predictive information for a spectrum of users and services. 10. Provide training opportunities to generate a sound knowledge base (and its transfer across generations) on polar prediction related issues. YOPP is implemented in three distinct phases. During the YOPP Preparation Phase (2013 through to mid-2017) this Implementation Plan was developed, which includes key outcomes of consultations with partners at the YOPP Summit in July 2015. Plans will be further developed and refined through focused international workshops. There will be engagement with stakeholders and arrangement of funding, coordination of observations and modelling activities, and preparatory research. During the YOPP Core Phase (mid-2017 to mid-2019), four elements will be staged: intensive observing periods for both hemispheres, a complementary intensive modelling and prediction period, a period of enhanced monitoring of forecast use in decisionmaking including verification, and a special educational effort. Finally, during the YOPP Consolidation Phase (mid-2019 to 2022) the legacy of data, science and publications will be organized. The WWRP-PPP Steering Group provides endorsement throughout the YOPP phases for projects that contribute to YOPP. This process facilitates coordination and enhances visibility, communication, and networking.

Description: The Year of Polar Prediction (YOPP) has the mission to enable a significant improvement in environmental prediction capabilities for the polar regions and beyond, by coordinating a period of intensive observing, modelling, prediction, verification, user- engagement and education activities. The YOPP Core Phase will be from mid-2017 to mid-2019, flanked by a Preparation Phase and a Consolidation Phase. YOPP is a key component of the World Meteorological Organization – World Weather Research Programme (WMO-WWRP) Polar Prediction Project (PPP). The objectives of YOPP are to: 1. Improve the existing polar observing system (better coverage, higher-quality observations); 2. Gather additional observations through field programmes aimed at improving understanding of key polar processes; 3. Develop improved representation of key polar processes in coupled (and uncoupled) models used for prediction; 4. Develop improved (coupled) data assimilation systems accounting for challenges in the polar regions such as sparseness of observational data; 5. Explore the predictability of the atmosphere-cryosphere-ocean system, with a focus on sea ice, on time scales from days to seasons; 6. Improve understanding of linkages between polar regions and lower latitudes and assess skill of models representing these linkages; 7. Improve verification of polar weather and environmental predictions to obtain better quantitative knowledge on model performance, and on the skill, especially for user-relevant parameters; 8. Demonstrate the benefits of using predictive information for a spectrum of user types and services; 9. Provide training opportunities to generate a sound knowledge base (and its transfer across generations) on polar prediction related issues. The PPP Steering Group provides endorsement for projects that contribute to YOPP to enhance coordination, visibility, communication, and networking. This White Paper is based largely on the much more comprehensive YOPP Implementation Plan (WWRP/PPP No. 3 – 2014), but has an emphasis on Arctic observations.

Description: Recent cold winter extremes over Eurasia and North America have been considered to be a consequence of a warming Arctic. More accurate weather forecasts are required to reduce human and socioeconomic damages associated with severe winters. However, the sparse observing network over the Arctic brings errors in initializing a weather prediction model, which might impact accuracy of prediction results at midlatitudes. Here we show that additional Arctic radiosonde observations from the Norwegian young sea ICE cruise project 2015 drifting ice camps and existing land stations during winter improved forecast skill and reduced uncertainties of weather extremes at midlatitudes of the Northern Hemisphere. For two winter storms over East Asia and North America in February 2015, ensemble forecast experiments were performed with initial conditions taken from an ensemble atmospheric reanalysis in which the observation data were assimilated. The observations reduced errors in initial conditions in the upper troposphere over the Arctic region, yielding more precise prediction of the locations and strengths of upper troughs and surface synoptic disturbances. Errors and uncertainties of predicted upper troughs at midlatitudes would be brought with upper level high potential vorticity (PV) intruding southward from the observed Arctic region. This is because the PV contained a ‘‘signal’’ of the additional Arctic observations as it moved along an isentropic surface. This suggests that a coordinated sustainable Arctic observing network would be effective not only for regional weather services but also for reducing weather risks in locations distant from the Arctic.

Description: During ice-free periods, the Northern Sea Route (NSR) could be an attractive shipping route. The decline in Arctic sea-ice extent, however, could be associated with an increase in the frequency of the causes of severe weather phenomena, and high wind-driven waves and the advection of sea ice could make ship navigation along the NSR difficult. Accurate forecasts of weather and sea ice are desirable for safe navigation, but large uncertainties exist in current forecasts, partly owing to the sparse observational network over the Arctic Ocean. Here, we show that the incorporation of additional Arctic observations improves the initial analysis and enhances the skill of weather and sea-ice forecasts, the application of which has socioeconomic benefits. Comparison of 63-member ensemble atmospheric forecasts, using different initial data sets, revealed that additional Arctic radiosonde observations were useful for predicting a persistent strong wind event. The sea-ice forecast, initialised by the wind fields that included the effects of the observations, skilfully predicted rapid wind-driven sea-ice advection along the NSR.

Description: The polar regions have been attracting more and more attention in recent years, fuelled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with less in situ observations, and with numerous local physical processes that are less well-represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere-sea ice-ocean models, even for short-term prediction; and insight into polar-lower latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting community will work together with stakeholders in a period of intensive observing, modelling, prediction, verification, user-engagement and educational activities.