ALBERT

Description: he Year of Polar Prediction (YOPP) is focused on a number of Special Observing Periods (SOPs) of two to three months’ duration in both the Northern and Southern Hemispheres. One feature of the SOPs is a program of enhanced radiosonde and buoy measurements contributing to assessments of how the extra observations support improved forecast skills. Another activity resulting from the SOPs is the YOPP Supersite Model Intercomparison Project (YOPPsiteMIP). The YOPPsiteMIP concept is unique compared to Climate Model Intercomparison Projects (CMIPs) because of (1) a focus on Numerical Weather Prediction (NWP) scales, processes and improvements, (2) the selection of polar, intensive, surface observing locations (YOPP Supersites) for which the NWP centers will create high-resolution model outputs and (3) special merged, observation outputs for the YOPP Supersites matched to the model outputs. These unique datasets of matched model time-step output and multi-variate high-frequency observations will enable detailed analysis of fast, small-scale processes. Example processes of interest are coupling mechanisms between the atmosphere, ocean, land and ice; cloud/precipitation micro- and macro-physics; the energy budgets over land, ocean and ice; and structure of the planetary boundary layer and the ocean mixed layer. The YOPPsiteMIP planning has focused so far on Arctic terrestrial YOPP Supersites, however, discussions are initiated on expanding the concept to selected Antarctic sites, as well as to ships and ice-stations in the Arctic Ocean. Preliminary results of the observation-model process-based evaluations at the YOPP Supersites relevant to environmental predictions ranging from weather to S2S scales will be presented.

Description: Recent studies have suggested that Arctic teleconnections affect the weather of the midlatitudes on time‐scales relevant for medium‐range weather forecasting. In this study, we use several numerical experimentation approaches with a state‐of‐the‐art global operational numerical weather prediction system to investigate this idea further. Focusing on boreal winter, we investigate whether the influence of the Arctic on midlatitude weather, and the impact of the current Arctic observing system on the skill of medium‐range weather forecasts in the midlatitudes is more pronounced in certain flow regimes. Using so‐called Observing System Experiments, we demonstrate that removing in situ or satellite observations from the data assimilation system, used to create the initial conditions for the forecasts, deteriorates midlatitude synoptic forecast skill in the medium‐range, particularly over northern Asia. This deterioration is largest during Scandinavian Blocking episodes, during which: (a) error growth is enhanced in the European‐Arctic, as a result of increased baroclinicity in the region, and (b) high‐amplitude planetary waves allow errors to propagate from the Arctic into midlatitudes. The important role played by Scandinavian Blocking, in modulating the influence of the Arctic on midlatitudes, is also corroborated in relaxation experiments, and through a diagnostic analysis of the ERA5 reanalysis and reforecasts.

Description: Polar regions are experiencing rapid climate change, faster than elsewhere on Earth with consequences for the weather and sea ice. This change is opening up new possibilities for businesses such as tourism, shipping, fisheries and oil and gas extraction, but also bringing new risks to delicate polar environments. Effective weather and climate prediction is essential to managing these risks, however our ability to forecast polar environmental conditions over periods from days to decades ahead falls far behind our abilities in the mid-latitudes. In order to meet the growing societal need for young scientists trained in this area, a Polar Prediction School for early career scientists from around the world was held in April 2016.

Description: Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state. The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric. Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño–Southern Oscillation.

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: Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice-edge error (IIEE), a user-relevant verification metric defined as the area where the forecast and the “truth” disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often-neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end-user relevant forecasts.

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.

Description: What: 120 scientists, stakeholders, and representatives from operational forecasting centers, international bodies, and funding agencies assembled to make significant advances in the planning of the Year of Polar Prediction; When: 13-15 July 2015; Where: WMO Headquarters, Geneva, Switzerland

Description: Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019.

Description: In the spring period of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, an initiative was in place to increase the radiosounding frequency during warm air intrusions in the Atlantic Arctic sector. Two episodes with increased surface temperatures were captured during April 12–22, 2020, during a targeted observing period (TOP).The large-scale circulation efficiently guided the pulses of warm air into the Arctic and the observed surface temperature increased from -30◦C to near melting conditions marking the transition to spring, as the temperatures did not return to values below -20◦C. Back-trajectory analysis identifies 3 pathways for the transport. For the first temperature maximum, the circulation guided the airmass over the Atlantic to the northern Norwegian coast and then to the MOSAiC site.The second pathway was from the south, and it passed over the Greenland ice sheet and arrived at the observational site as a warm but dry airmass due to precipitation on the windward side.The third pathway was along the Greenland coast and the arriving airmass was both warm and moist. The back trajectories originating from pressure levels between 700 and 900 hPa line up vertically, which is somewhat surprising in this dynamically active environment. The processes acting along the trajectory originating from 800 hPa at the MOSAIC site are analyzed. Vertical profiles and surface energy exchange are presented to depict the airmass transformation based on ERA5 reanalysis fields. The TOP could be used for model evaluation and Lagrangian model studies to improve the representation of the small-scale physical processes that are important for airmass transformation. A comparison between MOSAiC observations and ERA5 reanalysis demonstrates challenges in the representation of small-scale processes, such as turbulence and the contributions to various terms of the surface energy budget, that are often misrepresented in numerical weather prediction and climate models.