# The potential of numerical prediction systems to support the design of Arctic observing systems: Insights from the APPLICATE and YOPP projects

Irina Sandu, François Massonnet, Guillian van Achter, Juan Acosta Navarro, Gabriele Arduini, Peter Bauer, Ed Blockley, Niels Bormann, Matthieu Chevallier, Jonathan Day, et al.

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## To cite this version:

Irina Sandu, François Massonnet, Guillian van Achter, Juan Acosta Navarro, Gabriele Arduini, et al.. The potential of numerical prediction systems to support the design of Arctic observing systems: Insights from the APPLICATE and YOPP projects. Quarterly Journal of the Royal Meteorological Society, 2021, 147 (741), pp.3863-3877. 10.1002/qj.4182. hal-04950245

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DOI: 10.1002/qj.4182

Quarterly Journal of the
Royal Meteorological Society

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INVITED MANUSCRIPT

# The potential of numerical prediction systems to support the design of Arctic observing systems: Insights from the APPLICATE and YOPP projects

Irina Sandu<fnref n="1" /> | François Massonnet<fnref n="2" /> | Guillian van Achter<fnref n="2" /> | Juan C. Acosta

Navarro<fnref n="3" /> | Gabriele Arduini<fnref n="1" /> | Peter Bauer<fnref n="1" /> | Ed Blockley<fnref n="4" /> | Niels

Bormann<fnref n="1" /> | Matthieu Chevallier<fnref n="5" /> | Jonathan Day<fnref n="1" /> | Mohamed Dahoui<fnref n="1" /> | Thierry

Fichefet<fnref n="2" /> | Daniela Flocco<fnref n="6" /> | Thomas Jung<fnref n="7" /> | Ed Hawkins<fnref n="6" /> | Stephane

Laroche<fnref n="8" /> | Heather Lawrence<fnref n="1" />,<fnref n="4" /> | Jørn Kristiansen<fnref n="9" /> | Eduardo

Moreno-Chamarro<fnref n="3" /> | Pablo Ortega<fnref n="3" /> | Emmanuel Poan<fnref n="8" /> | Leandro Ponsoni<fnref n="2" /> | Roger

Randriampianina<fnref n="9" />

Correspondence

I. Sandu, European Centre for
Medium-Range Weather Forecasts
(ECMWF), Shinfield Park, Reading, RG2
9AX, UK.

Email: irina.sandu@ecmwf.int

Funding information

European Commission, Grant/Award
Number: 727862; Norges Forskningsråd,
Grant/Award Number: 280573

Abstract

Numerical systems used for weather and climate predictions have substantially improved over past decades. We argue that despite a continued need for further addressing remaining limitations of their key components, numerical prediction systems have reached a sufficient level of maturity to examine and critically assess the suitability of Earth's current observing systems – remote and *in situ*, for prediction purposes; and that they can provide evidence-based support for the deployment of future observational networks. We illustrate this point by presenting recent, co-ordinated international efforts focused on Arctic observing systems, led in the framework of the Year of Polar Prediction and the H2020 project APPLICATE. The Arctic, one of the world's most rapidly changing regions, is relatively poorly covered in terms of *in situ* data but richly covered in terms of satellite data. In this study, we demonstrate that existing state-of-the-art datasets and targeted sensitivity experiments produced with numerical prediction systems can inform us of the added value of existing or even hypothetical Arctic observations, in the context of predictions from hourly to interannual time-scales. Furthermore, we argue that these datasets and experiments can also inform us how the uptake of Arctic observations in numerical prediction systems can be enhanced to maximise predictive skill. Based on these efforts we suggest that (a) conventional *in situ* observations in the Arctic play a particularly important role in initializing numerical weather forecasts during the

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2021 Crown copyright, Met Office. © 2021 Her Majesty the Queen in Right of Canada. © 2021 ECMWF. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland. Reproduced with the permission of the Minister of Environment and Climate Change Canada.

<footnote n="1">¹European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK</footnote>

<footnote n="2">²Université Catholique de Louvain, Brussels, Belgium</footnote>

<footnote n="3">³Barcelona Supercomputing Centre, Barcelona, Spain</footnote>

<footnote n="4">⁴Met Office, Exeter, UK</footnote>

<footnote n="5">⁵Meteo-France, Toulouse, France</footnote>

<footnote n="6">⁶University of Reading, Reading, UK</footnote>

<footnote n="7">⁷Alfred Wegener Institute, Bremerhaven, Germany</footnote>

<footnote n="8">⁸Environment and Climate Change, Gatineau, Quebec Canada</footnote>

<footnote n="9">⁹Met Norway, Oslo, Norway</footnote>
winter season, (b) observations from satellite microwave sounders play a par-
ticularly important role during the summer season, and their enhanced usage
over snow and sea ice is expected to further improve their impact on predictive
skill in the Arctic region and beyond, (c) the deployment of a small number of *in
situ* sea-ice thickness monitoring devices at strategic sampling sites in the Arctic
could be sufficient to monitor most of the large-scale sea-ice volume variability,
and (d) sea-ice thickness observations can improve the simulation of both the sea
ice and near-surface air temperatures on seasonal time-scales in the Arctic and
beyond.

KEYWORDS

Arctic, climate prediction, data assimilation, *in situ* measurements, numerical modelling, observing system design, satellite information, weather forecasting

1 | INTRODUCTION

Environmental changes in the Arctic open up new oppor-
tunities (e.g. new shipping routes), but also bring new
challenges (e.g. environmental disasters such as oil spills).
With the ever-growing interest in Arctic regions, the need
to improve predictions from hours to years ahead and
the need for near-real-time polar weather and climate
monitoring all become more pressing than ever. In polar
regions, however, producing such predictions is even more
difficult than in other regions due to specific challenges
related to process understanding (including those unique
to the polar regions like rapid Arctic air-mass transforma-
tions: Pithan et al., 2018), coupled modelling (e.g. repre-
sentation of stable boundary layers, mixed-phase clouds,
snow and sea ice, and their coupling with the atmosphere),
data assimilation and observations (e.g. Bauer et al., 2014;
Bauer and Jung, 2016; Jung et al., 2016).

It is often argued that better predictions will be made
possible by improving the underlying numerical mod-
els. While this is correct, it should be remembered that
weather and (initialized) climate predictions rely on
four building blocks: a numerical model to represent the
dynamical and physical processes of the Earth system;
a comprehensive set of observations; a data assimilation
scheme to create the best possible estimate of the state of
the atmosphere, land, ocean and sea ice from which pre-
dictions are initialized, by optimally blending the model
and the observations; and a methodology to generate an
ensemble of simulations to reflect forecast uncertainty.
Hereafter, numerical prediction systems (NPS) refer to
the numerical systems relying on these four building
blocks, which are used to produce weather and climate
predictions.

Observations are crucial for weather predictions,
due to their importance for deriving accurate initial

conditions and for evaluating the performance of the
weather forecasts. Improvements in the quality and cov-
erage of observations and in their usage in NPS have
undeniably contributed to the tremendous advances in
numerical weather prediction capabilities which have
taken place over recent decades (Bauer et al., 2015). Sim-
ilarly, observations have been instrumental to advance
climate prediction capabilities by assisting the develop-
ment of new physical components (e.g. Hunke et al. (2010)
for sea ice modelling); the choice of parameters in phys-
ical parametrizations and the identification of system-
atic model errors, which has revealed increased realism
of the climate models from one Coupled Model Inter-
comparison Project (CMIP) cycle to the next (e.g. Notz
et al., 2020).

To improve weather and climate predictions in the
Arctic, enhancing the Arctic observing systems and fur-
ther developing their use in NPS are as important as
further developing the numerical models themselves to
reduce remaining systematic biases. In this context, sev-
eral questions related to Arctic observations necessitate
urgent answers:

* To which degree do we make optimal use of currently available Arctic observations in numerical prediction systems?

* What cost-effective strategies can be used to reliably monitor the large-scale Arctic climate variability from *in situ* observations alone?

* What is the importance of the currently available Arctic observing systems for predictive skill in the Arctic and beyond from hours to years ahead?

* Are Arctic observations more beneficial for skill during certain types of weather regimes, as has been found for midlatitudes?
* What new Arctic observations would we need to further enhance predictive skill in the Arctic and beyond?

A concerted international effort to address these ques-
tions was made in recent years, in the framework of the
Polar Prediction Project (PPP) of the World Meteorologi-
cal Organization (WMO) and of its core activity the Year
of Polar Prediction (YOPP), as well as the Horizon 2020
European project APPLICATE (www.applicate-h2020.eu).
In these initiatives, NPS have played a central role.
The investigations regarding the role and design of Arc-
tic observing systems primarily relied on state-of-the-art
datasets produced with NPS (e.g. analyses and reanalyses,
climate historical reconstructions, initialized predictions
and projections) and on targeted numerical experimenta-
tion using these systems.

This approach relies on the fact that, for the reasons
detailed in the next section, NPS can now be reliably used
to answer key questions related to the observing systems
thanks to the advances made in the last decades (Bauer
et al., 2015; Zampieri et al., 2018). NPS have indeed already
been previously successfully used to assess the relative
role of various observations on weather forecasting skill
at a global scale (e.g. Thépaut and Kelly, 2007; Bauer and
Radnoti, 2009; Radnoti, 2010; Radnoti et al., 2010; 2012;
Bormann et al., 2019). They have also been used to deter-
mine the importance of certain Arctic observations for
predictive skill from hours to seasons ahead (e.g. Inoue
et al., 2009; 2013; Day et al., 2014; Massonnet et al., 2015;
Yamazaki et al., 2015; Ono et al., 2016; Sato et al., 2017;
Bushuk et al., 2019).

The effort conducted in the framework of YOPP and
APPLICATE embraced this approach, focusing specifi-
cally on guiding the design of Arctic observing systems
using NPS and emerging statistical techniques. It also
searched to further demonstrate the idea that NPS can
be successfully used to answer such questions for the
Arctic, despite remaining challenges that need to be tack-
led in these regions related to systematic model errors
(e.g. cloud microphysics, stable boundary layers, sea-ice
thickness), data assimilation and ensemble techniques,
and observation usage. This effort relied on a variety of
NPS-based techniques and a diversity of NPS, applied in a
concerted manner employing co-ordinated protocols, and
was made possible thanks to international cooperation.
To our knowledge, a dedicated effort to answer questions
related to the optimal design of Arctic observing systems to
maximize predictive skill across time-scales has not been
previously attempted.

This article gives an overview of this effort and uses
its key findings to answer the questions posed above and
to draw recommendations on how to enhance the Arctic
observing systems and the uptake of Arctic observations

in coupled NPS. First, we briefly review the available evi-
dence suggesting that NPS can be successfully used to
derive insights regarding Arctic observing systems. We
then discuss the approaches used to derive these insights,
before making a few recommendations on how to enhance
the design, and future exploitation, of the Arctic observing
systems and before presenting some conclusions.

2 | WHY AND HOW CAN NPS BE USED TO INFORM ARCTIC OBSERVING SYSTEM DESIGN?

The numerical systems used to produce weather fore-
casts, analyses and reanalyses have massively improved
over the last decades. Advances in weather prediction
represent a quiet revolution because they have resulted
from a steady accumulation of progress in fundamental
science on different fronts (numerical techniques, phys-
ical parametrizations, data assimilation methodologies),
from the use of vast amounts of observations and from an
exponential growth in supercomputing capacities (Bauer
et al., 2015). This quiet revolution has resulted in today’s
forecasts of large-scale weather patterns 6 days ahead
being as good as forecasts 4 days ahead 20 years ago. It also
led to considerable improvements of long-term reanaly-
ses, which constitute the best possible reconstruction of
the past atmospheric state obtained by blending a forecast
model and observations through data assimilation with
the same NPS used to produce weather forecasts. Mod-
ern reanalyses such as those produced by the Copernicus
Climate Change Service (C3S) spanning the last 70 years
(e.g. ERA5: Hersbach et al., 2020) are crucial tools for
assessing and monitoring the climate and the changes it
experienced over recent decades.

In parallel to the weather prediction revolution, cli-
mate models have also steadily improved. Weather and
climate models are sharing an increasing number of com-
mon elements (e.g. atmosphere and ocean dynamical
cores, parametrizations of atmospheric processes, ocean
physics, sea ice physics, land models, atmospheric com-
position (Brown *et al.*, 2012)). There is also an interest in
using diagnostic tools typically used in numerical weather
prediction to assess the realism of the representation of
weather-type phenomena in climate models, also in sup-
port of judging whether the climate sensitivity produced
by certain models can be considered credible (Palmer
*et al.*, 2008; Hoskins, 2013; Palmer, 2020). These methods
clearly emphasise the benefits of seamless thinking and
the generic applicability of data assimilation methods for
exploiting rich observational datasets.

The drive towards a more unified (seamless) approach
for weather and climate modelling is further fuelled
by similar scientific and computing challenges, but also
by a growing interest for initialized predictions from
sub-seasonal to seasonal and decadal scales. Similar to
medium-range forecasts, predictions at sub-seasonal and
seasonal time-scales have also improved considerably over
the past decades (Stockdale et al., 2018; Robertson and
Vitart, 2019). Furthermore, important improvements start
to emerge in decadal prediction (Smith et al., 2019), includ-
ing in the Arctic.

Given the increasing interest in Arctic regions, tar-
geted efforts have been made to assess the quality of
weather forecasts and of long-term global or regional
reanalyses in polar regions (Jung and Leutbecher, 2007;
Bauer et al., 2014; Bromwich et al., 2016; Jung and Mat-
sueda, 2016; Batrak and Müller, 2019; Graham et al., 2019;
Vessey et al., 2020; Renfrew et al., 2021). These studies have
demonstrated that despite the specific challenges (Jung
et al., 2016) and remaining issues that need to be addressed
in polar regions in the four building blocks of NPS (Sandu
et al., 2021), short- and medium-range weather forecasts
and reanalyses have improved over time in the Arctic
(Bauer et al., 2014; Jung and Matsueda, 2016). The skill
of medium-range forecasts in the Arctic has for example
improved at a similar pace, albeit it remains lower than in
the midlatitudes (e.g. see figure 1 of Sandu et al. (2021)).
Similarly, despite remaining challenges, the prospects for
sub-seasonal to seasonal sea ice predictions in the Arctic
also look bright (Blockley and Peterson, 2018; Zampieri
et al., 2018). Some forecasts conducted with dynamical
models now outperform trivial benchmarks (climatologi-
cal or anomaly persistence forecasts) and new techniques
based on data-driven approaches show promising results,
too (Andersson et al., 2021).

These improvements in predictive skill corroborate the
increased quality of NPS in Arctic regions, giving confi-
dence that the datasets they produce, including the out-
put from specifically designed numerical experimentation,
can be used to answer questions regarding Arctic observ-
ing system design. This argument will be further substan-
tiated by some concrete examples given below.

In the studies reviewed below, the questions posed in
the Introduction have been addressed with various meth-
ods using NPS that can be categorised in two groups:
(a) in-depth analyses of existing, consolidated datasets (in
particular analyses, reanalyses, and CMIP5 and CMIP6
historical runs), and (b) targeted numerical experimenta-
tion. Some of these targeted experiments aimed at assess-
ing the impact of different atmospheric observations on
short- and medium-range weather forecasts (i.e. day–week
time-scales), while others explored the impact of ini-
tialization of different sea ice variables through differ-
ent techniques on predictions a few months ahead (i.e.
sub-seasonal to seasonal time-scales). Regardless of the

time-scale, it was important to ensure that the results were
not specific to a given system. This is why the experimenta-
tion was performed with several NPS, and in certain cases
it was co-ordinated to follow similar protocols and cover
similar time periods.

In the following, we briefly summarise these efforts
and point to the studies describing each of them in more
detail. Figure 1 encapsulates in a nutshell the key results
of these studies, used to formulate the recommendations
for the enhanced Arctic observing system design and its
exploitability in NPS.

3 | ANALYSIS OF EXISTING DATASETS

3.1 | Examining the (sub)optimal usage of Arctic atmospheric observing systems

A commonly made – and yet incorrect – assumption is that the Arctic is void of atmospheric observations. It is true that conventional observations, such as those gathered by national meteorological services through radiosonde launches, surface stations or deployment of buoys in the ocean or over the sea ice, are comparatively sparser north of 60°N than at lower latitudes. However, despite the lack of geostationary satellite data, polar regions are rather well covered by satellite observations compared to lower-latitude regions, thanks to the strong overlap of orbits of Low Earth Orbit (LEO; or polar-orbiting) satellites.

A lot can be learned about Arctic atmospheric observ-
ing systems by studying the analyses used to initialize
numerical weather predictions (Lawrence et al., 2019b).
Figure 2 illustrates the point above by showing the num-
ber of atmospheric conventional and satellite observations
assimilated in the ECMWF Integrated Forecasting Sys-
tem (IFS) for creating the initial conditions of weather
forecasts in 2019. Opposing Equator–Pole gradients can
indeed be seen for conventional and satellite observations,
highlighting a decrease/increase of conventional/satellite
observations from the Equator to the Poles. Another strik-
ing feature of the observational data distribution shown
in Figure 2 is that a large number of observations are
rejected (i.e. not assimilated) either because of too large
model-data mismatch, or because of difficulties in treating
observational uncertainties. Although this is a feature of
all latitudes/seasons, this phenomenon is amplified in the
Arctic.

Lawrence *et al.* (2019b) also showed that there is a
reduced use of satellite radiances from channels peaking in
the lower troposphere during winter in the ECMWF IFS,
particularly over snow and sea ice (e.g. see their figure 2).
<figure><img src="image_3.png" /><figcaption>**FIGURE 1** Key results of the co-ordinated efforts exploiting numerical prediction systems to inform the design and enhanced exploitability of the Arctic observing systems. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**FIGURE 2** Availability and actual usage of conventional and satellite observations in the ECMWF IFS. Number of available observations (per 1,000 km² and per day) for different latitudes bands of the Northern Hemisphere (light shades) and the number of observations actually assimilated in the IFS (darker shades) to create the initial conditions of the weather forecasts. The data correspond to January 2019 (left) and July 2019 (right). Note the logarithmic scale. [Colour figure can be viewed at wileyonlinelibrary.com]</figcaption></figure>
This is possibly related to issues in (a) the modelling of snow (Arduini et al., 2019), sea ice, mixed-phase clouds and shallow stable boundary layers, (b) the assumptions regarding the surface emission and reflection over sea ice and snow, made in the radiative transfer computations used to project model variables into satellite observation space (radiances), and (c) the associated specification of background error covariances which is important for giv-ing appropriate weights to the observations and model fields in the data assimilation. It was found, for example, that the weights given to observations in the Arctic lower troposphere and stratosphere are currently too low, which suggests that the adjustments that the observations can make in these regions when creating the initial condi-tions are currently limited (for more details, see Lawrence et al. (2019b)).

The analysis of operational diagnostics performed from the ECMWF data assimilation system, such as analysis increments, observation minus short-range forecasts, or adjoint-based techniques like the Forecast Sensitivity Observations Impact (FSOI: Cardinali, 2009) demonstrated the value of all Arctic atmospheric observations for the skill of the analyses and short-range forecasts (Lawrence et al., 2019b). The FSOI analysis for example suggested that among the data available in the Arctic, the conventional observations contribute the most to reducing the global short-range forecast error during winter, while microwave observations from polar-orbiting satellites contribute the most in summer.

## 3.2 | Defining strategies for monitoring Arctic sea-ice volume variability

An analysis of the simulated sea-ice thicknesses in state-of-the-art climate models participating in the High Resolution Model Intercomparison Project (High-ResMIP: Haarsma et al., 2016) of CMIP6 has revealed that the variability of this field exhibits significant spatial auto-correlation if grid-cell averages and monthly means are considered. This finding suggests that a limited number of well-placed *in situ* monitoring stations could be sufficient to estimate the large-scale changes in sea-ice thickness and volume on interannual time-scales (Ponsoni et al., 2020), especially if the *in situ* data records are complemented and cross-validated by large-scale satellite retrievals, for which large uncertainties remain (Zygmuntowska et al., 2014). A proposed list of 10 sampling sites is given in Ponsoni et al. (2020) and is illustrated in Figure 1 (middle panel). According to their study, sea-ice thickness measurements sampled from as little as six well-placed stations are sufficient to reconstruct most (80%) of the actual sea-ice volume variability. Among

these regions, they find the transition between the Beaufort, Chukchi and Central Arctic Seas (a region known as the “sea ice factory” of the Arctic), the North Pole, and the boundary between the Laptev Sea and the Central Arctic Ocean (that is, across the transpolar drift), to be top-priority sites where sea-ice thickness should be monitored.

The results should be interpreted in full awareness of the potential limitations of current general circulation models. Although NPS are our best tools for guiding observing system design, uncertainty in the decorrelation length-scales of thickness anomalies seen in coupled general circulation models (Blanchard-Wrigglesworth and Bitz, 2014) and in reanalyses (Ponsoni et al., 2019) imply that the number and location of optimal sites for thickness monitoring may themselves be uncertain. This coherence might not correspond to reality for several reasons, including the lack of mechanical redistribution of sea ice in many of those models, or more simply the lack of an explicit subgrid-scale sea-ice thickness distribution. It is also worth noting that most of the sea ice models within CMIP6 models are based on the same assumptions (e.g. viscous-plastic assumption for the sea ice rheology). Such a lack of model diversity can cause an artificial robustness of the results.

This uncertainty is projected to narrow down in the future as models improve, for example, by running at higher spatial resolution, by benefiting from improved tuning of critical parameters or by accounting for more subgrid-scale processes like floe size distribution, form drag or snow. On top of that, the practicality of the Ponsoni et al. conclusions is limited by the fact that a measuring station at a fixed sampling site would never measure sea-ice thickness that is representative of the average conditions several dozens of km around it (which is what CMIP6 models provide), see for example Geiger et al. (2015).

# 4 | CO-ORDINATED NUMERICAL EXPERIMENTATION

## 4.1 | Assessing the impact of atmospheric observations on short-to medium-range predictive skill

Another important effort exploited so-called Observing System Experiments (OSEs), in which certain observations are withdrawn (denied) from data assimilation when creating the initial conditions for weather forecasts. Such experiments allow measuring the actual impact of losing certain observing systems and evaluating the influence on the medium-range forecast for a range of parameters. OSEs in which different types of atmospheric observations
were denied in polar regions were performed for the first time in a co-ordinated way at several operational weather centres, including ECMWF, Environment and Climate Change Canada (ECCC), German Weather Service (DWD) and Met Norway. OSEs were performed for several seasons, among which the YOPP Special Observing Periods for the Northern Hemisphere (February to March and June to August 2018), and the analysis focused mainly on the impact of Arctic observations. The results highlighted the added value of Arctic observations for short- and medium-range forecast skill (Lawrence et al., 2019a; Laroche and Poan, 2021; Randriamampianina et al., 2021) by showing that:

* In global NPS, all current Arctic atmospheric observing systems increase short- and medium-range predictive skill both in polar regions and midlatitudes;

* In all the contributing global NPS, conventional Arctic observations have the largest impact in winter, while in summer the leading observing system varies from one forecasting system to another: radiances from microwave sounders play the biggest role in the ECMWF system, while conventional observations are most important at ECCC and DWD. This demonstrates that observation impacts are always subject to the sophistication/maturity of the data assimilation system, the forecast model and of the assessment and monitoring of the observations' quality;

* The use of microwave sounder observations in the ECMWF IFS is suboptimal during winter. As Lawrence et al. (2019b) demonstrated, fewer microwave observations are assimilated during winter than during summer, particularly over snow and sea ice. The strong positive impact of microwave observations on predictive skill in summer suggests that improving their use over snow and sea ice is likely to further improve forecasts in the Arctic and the midlatitudes, particularly during the winter season (Figure 1).

* In regional NPS (such as AROME-Arctic), there is a clear benefit in terms of short-range forecast skill from the assimilation of Arctic atmospheric observations both in the global models used to create their lateral boundary conditions, and in the regional NPS themselves (Randriamampianina et al., 2021).

## 4.2 | Assessing the impact of sea-ice thickness initialization on seasonal forecasts

A parallel effort consisted of using numerical experimentation to explore the impacts of the sea-ice thickness

initialization on predictive skill of the summer sea ice cover and atmospheric circulation. Arctic sea ice reanalyses are well constrained when it comes to their areal extent, because the assimilated datasets of sea ice concentration are available on a daily basis and at the large scale and are fairly mature. However, these reanalyses are highly scattered for thickness which is not yet routinely assimilated (Chevallier et al., 2017; Uotila et al., 2019). So initializing the ocean-sea-ice models from existing sea ice reanalyses might introduce initial-condition errors that will eventually translate into forecast errors.

To overcome this issue, several approaches were used in a co-ordinated manner to investigate the benefit of assimilating sea-ice thickness information. First, Blockley and Peterson (2018) assimilated sea-ice thickness, from CryoSat-2, in the Met Office GloSea coupled atmosphere-ocean-sea ice prediction system and showed that the September sea ice concentration was more successfully predicted than in experiments initialized without thickness assimilation, with a reduction in ice-edge error of around 37% (in both cases, the forecasts were initialized for a range of dates in late April and early May). These results have recently been confirmed with another coupled atmosphere-ocean-sea ice forecasting system (EC-Earth3) for the same pair of months, with the assimilation of observed sea ice freeboards employing an ensemble Kalman filter, for the year 2012. In these experiments, it was found that applying an observational (CryoSat-2) constraint on the 1 May sea ice freeboard would be sufficient to reduce the September integrated ice edge error, a measure of spatial disagreement between forecasted sea ice concentration and that of the verification dataset, by up to 25% (Massonnet et al., in preparation). These advances have undeniably contributed to bridge the gap between earlier theoretical studies of sea ice predictability (e.g. Day et al., 2014) that demonstrated the importance of the knowledge of spring sea-ice thickness information, and full, quasi-operational seasonal predictions with all the constraints that this implies (existence of model drift, observational errors, need to work with large ensembles). Above all, these results motivate the need for continued monitoring of radar and laser altimetry measurements of sea-ice thickness. Thin ice measurements (e.g. through passive microwave imagery from the Soil Moisture and Ocean Salinity mission from the European Space Agency (ESA)) will also be needed to initialize models in peripheral seas (Yang et al., 2014).

Second, a two-model co-ordinated experiment using the Hadley Centre Global Environment Model version 3 (HadGEM3) and EC-Earth3 global coupled models was conducted to assess the role of Arctic winter sea-ice thickness information on seasonal to annual predictability (Flocco et al., in preparation). By following a data-denial
<figure><img src="image_5.png" /></figure>

approach in idealised set-ups, it was shown that neglecting the initial January sea-ice thickness information in these prediction systems led to a systematic reduction in prediction skill for sea ice variables, but also for air temperature in that month (as expected) and in September (less expected). This finding supports the existence of long-range predictability mechanisms through re-emergence, that is, an increase in auto-correlation of time series after an initial decrease, confirming earlier studies (e.g. Blanchard-Wrigglesworth et al., 2011; Bushuk and Giannakis, 2017). More importantly, the results obtained underlined the need to sustain large-scale measurements of sea-ice thickness with satellite campaigns during winter (Figure 1).

# 5 | HOW SHOULD THE ARCTIC OBSERVING SYSTEMS BE ENHANCED?

## 5.1 | Enhancements of the Arctic observing systems in YOPP

The OSEs performed by Lawrence et al. (2019a) demonstrated that removing Arctic atmospheric *in situ* or satellite observations during the data assimilation process used to create the initial conditions for the forecasts, deteriorates midlatitude synoptic forecast skill in the medium range, particularly over northern Asia. Day et al. (2019) further analysed these OSEs and showed that the 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 more effectively propagate from the Arctic into midlatitudes. The important role played by Scandinavian blocking in modulating the influence of the Arctic on midlatitudes is further corroborated in so-called relaxation experiments, in which the state variables are relaxed, or nudged towards an atmospheric reanalysis throughout the forecast range, and through a diagnostic analysis of the ERA5 reanalyses and reforecasts.

The idea that the influence of the Arctic on midlatitudes is flow-dependent was proposed in the studies of Jung et al. (2014) and Semmler et al. (2018) using a relaxation approach. The Day et al. (2019) study corroborates these results and, importantly, further demonstrates that the periods when the Arctic has a strong influence on northern Asia are also periods when the midlatitudes have a strong influence on the Arctic. In particular, during Scandinavian blocking episodes, the crests of planetary waves extend into the Arctic causing high baroclinicity along warm intrusions and associated

rapid error growth. This regime also provides a mechanism for errors to be propagated out of the Arctic as well. Although Day et al. (2019) have focused on such patterns over Eurasia, it is likely that similar high-amplitude planetary waves in the Pacific–North-American sector would lead to a similar influence over North America, as also suggested by Yamazaki et al. (2021). Day et al. (2019) suggested that increasing the observational coverage in regions of high error growth in the European Arctic during Scandinavian blocking events should improve forecast errors, not just in this part of the Arctic, but also downstream over northern Asia. Indeed, such flow-dependent error growth suggests that a more dynamic observing network, where more observations are taken in regions and during weather conditions for which error growth is fast, might be advantageous. In fact, this is reminiscent of the targeted observation efforts carried out in THe Observing system Research and Predictability Experiment (THORPEX) (Parsons et al., 2017). It looks that the Arctic is particularly well suited to this concept (which has been largely abandoned in recent years for midlatitudes).

These results partly contributed to the change in approach that was adopted for the third period of enhanced Arctic observations conducted as part of YOPP. This took the form of a targeted observing period (TOP), which was different from the earlier special observing periods (SOPs) held in 2018, during which additional radiosondes were released around the whole Arctic every day for an extended period of time. In the TOPs, extra observations were only requested during selected meteorological situations of enhanced relevance for the Arctic. Additional radiosondes were launched from different stations situated along warm air intrusions in order to shed light on the processes governing these situations, which were shown by Day et al. (2019) to be particularly hard to predict. During the period of this warm air intrusion event, when the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) ice camp was located north of Svalbard, four radiosondes per day were launched at several upstream locations and seven per day on board the *Polarstern*.

## 5.2 | Long-term enhancements of the conventional observational networks

Results of the OSEs performed at ECMWF and other centres in the framework of YOPP, H2020 APPLYCATE and Alertness (a project led by Met Norway) have demonstrated the importance of conventional observations in the Arctic, particularly for the winter season during which they were found to be the leading observing system relative to microwave radiances, infrared radiances, Global
<figure><img src="image_6.png" /></figure>

Positioning System Radio Occultation (GPSRO) and atmospheric motion vectors (AMVs). This impact is particularly impressive given that conventional observations make up only 4–6% of all assimilated observations north of 60°N (Figure 2). Results of the OSEs suggest that radiosonde data are responsible for approximately half of the overall impact of conventional data in the ECMWF and ECCC forecasting systems, indicating that these observations are particularly important. While these observations are more expensive to obtain at high latitudes, these data are needed for reducing forecast errors in the Arctic winter and a key recommendation is that these sites should be maintained. Where possible, extending the conventional network, in particular in areas where there are currently no conventional observations, is also expected to lead to improved forecasts.

The analysis of climate model simulations has revealed the possibility of generating large-scale reconstructions of sea-ice volume from a small number of well-placed sampling devices measuring sea-ice thickness (Ponsoni et al., 2020). This idea is particularly appealing, given that in the real world deploying conventional sampling sites comes at a cost (human, financial, logistical). More out-of-sample tests are required, for example, with independent sea ice reanalyses, other types of numerical simulations, higher-resolution output, to test whether the 10 stations highlighted in Figure 1 are sufficient to reconstruct the real-world sea-ice volume anomalies. Even if this result proves to be robust with other datasets, we should keep in mind that NPS cannot represent metre-scale variations of sea-ice thickness, which will provide a challenge when turning this idea of a minimal number of stations into a concrete campaign strategy. In addition, sea ice is a highly mobile medium so that mooring drifting buoys to the sea ice cover would probably not be enough to sample sea-ice thickness in a spatially representative way. There are (Eulerian) alternatives to buoys, like bottom-mounted upward-looking sonars (Figure 2) or airborne remote sensing of sea-ice thickness from low-altitude flying aircraft (e.g. NASA Operation IceBridge: Kurtz et al., 2013), which could provide the desired sea-ice thickness estimates that, averaged together, would better match the model spatial scales.

# 6 | ENSURE UNINTERRUPTED SATELLITE MONITORING AND ENCOURAGE BACK-EXTENSIONS OF THE SATELLITE RECORD

The studies described above demonstrate the value of current polar satellite observations – both sampling the atmosphere and the sea ice – for predictive skill from

hours to seasons ahead both in the Arctic and midlatitudes. The key recommendation here is to ensure the continued monitoring from space of key variables in the atmosphere, ocean and sea ice. With the scheduled end of the ICESat2, Sentinel-3 and CryoSat2 missions in 2024–2025 and the start of the ESA Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) mission only in 2027 at the earliest, there is likely to be a 2–3-year gap in Arctic sea-ice thickness monitoring. Even though current operational seasonal prediction systems do not assimilate sea-ice thickness information, this might well be the case in several years from now. The unavailability of such valuable thickness information could adversely affect the quality of seasonal predictions. For microwave sounding observations, we currently benefit from many old satellites that are operating well beyond their design life, and the benefit from having the present range of satellites has been highlighted by Duncan and Bormann (2020) globally as well as for the Arctic region. It is recommended to maintain or even improve the available sampling, including through initiatives such as the Arctic Weather Satellite (www.esa.int/aws).

In parallel to these efforts in sustaining the existing infrastructure, there is an increasing demand to develop innovative approaches to extend the satellite record of sea-ice thickness back in time for as long as possible. According to the pioneering work of Laxon et al. (2003), estimates of Arctic sea-ice thickness could be obtained as early as 1993 and the early days of radar altimetry, with the launch of the European Remote Sensing (ERS) 1 and 2 satellites on which a Ku radar altimeter was installed. Such a long-term back-extension of the sea-ice thickness data would be a significant advancement, allowing operational centres to perform seasonal hindcasts over significantly longer reforecast periods, thus allowing better estimating the lead-dependent forecast biases and in this way improve the a *posteriori* correction of forecasts. We strongly encourage future work that would contribute to producing such a back-extension of sea-ice thickness.

# 7 | HOW TO ENHANCE THE USE OF EXISTING OBSERVATIONS

## 7.1 | Improved use of available atmospheric satellite observations

The OSEs performed for atmospheric observations (e.g. Lawrence et al., 2019a) also allowed identifying key areas of development for enhancing the use of atmospheric satellite observations and thereby improving forecast skill in both the Arctic itself and the midlatitudes.

While microwave radiances in summer were found to be the most important part of the observing systems
in the Arctic, they have a reduced impact in winter relative to other observation types, primarily due to their suboptimal assimilation over snow and sea ice. The assimilation of microwave radiances over snow and sea ice could be improved through better modelling of snow properties (depth, density, temperature, albedo, emissivity), both in the physical model of the snow and in forward models used to represent satellite observations accurately in snow-covered regions. Building blocks to make these improvements are already being developed. These include improvements in snow models (e.g. the multi-layer snow model such as that developed by Arduini et al. (2019)), as well as fast and accurate radiative transfer models for snow that can simulate the full array of satellite observations available. A number of such models have been developed (Wiesmann and Mätzler, 1999; Lemmetyinen et al., 2010; Picard et al., 2013) and, as a first step, efforts are needed to evaluate them for use in NWP. Recently, the Helsinki University of Technology (HUT) model developed by Lemmetyinen et al. (2010) has been evaluated at ECMWF for low-frequency microwave observations (6–10 GHz: Hirahara et al., 2020). Crucially, however, evaluation is also needed at higher frequencies (50–60 GHz, 183–190 GHz). The radiative transfer forward model over snow and sea ice, for which there are known problems with the current treatment of surface emission and reflection (as discussed by Lawrence et al., 2019a), should also be improved. In operational weather prediction centres, the surface emission/reflection is usually treated as specular and the emissivity and/or skin temperature over land and sea ice is retrieved prior to assimilation from a window channel. This method is subject to higher errors over snow and sea ice than over snow-free land, leading to higher systematic differences between observations and forecasts and a suboptimal use of the data, as discussed by Lawrence et al. (2019a). Again, solutions such as using a representation of Lambertian surface effects are being tested at the moment and will be reported on in future studies.

Infrared radiances were also found to have a positive impact on forecasts in the Arctic at ECMWF, and improvements in the use of these observations are also likely to lead to benefits to forecasts in the Arctic and midlatitudes. At ECMWF, non-surface sensitive infrared observations were recently added over land leading to a large positive impact on short- and medium-range forecast skill (Eresmaa et al., 2017). Developments in using tropospheric channels over land are therefore likely to lead to improvements in forecast skill in the Arctic. This would require accurate modelling of the surface emission and reflection for infrared frequencies, to support the addition of these channels over land. In addition, cloud detection in the infrared remains challenging over cold surfaces (Jeppeisen et al., 2019) as is the representation of mixed-phase

clouds, and dedicated attention to these aspects is recommended.

## 7.2 | Improved use of sea ice observations

The EC-Earth3 and HadGEM3 coupled systems are now capable of assimilating a wealth of polar observations (sea ice concentration, seawater temperature and salinity, sea-ice thickness and sea ice freeboard) using different assimilation approaches, from nudging and flux adjustment methods to variational approaches (3D-Var) and sequential approaches (ensemble Kalman filter). Sensitivity experiments have clearly demonstrated that the uptake of sea ice observations in these climate prediction systems is beneficial for the skill in Arctic seasonal predictions, corroborating the results obtained by Blockley and Peterson (2018) with an operational seasonal prediction system, Met Office Global Seasonal (GloSea). These results demonstrate the importance of sea-ice thickness initialization for predictions up to several months ahead and pave the way for assimilating these observations in the next generation of operational seasonal prediction systems.

# 8 | CONCLUSIONS

The work conducted in the framework of APPLICATE and YOPP reviewed here has demonstrated that NPS and ensuring datasets can be successfully used to extract a wealth of information regarding the impacts of existing Arctic observations, and define pathways to improve their uptake in prediction systems and the design of future observing systems.

It has allowed us to answer, at least in part, the questions regarding Arctic observing system design and exploitability posed in the Introduction:

* To which degree do we make optimal use of currently available Arctic observations in numerical prediction systems?

The uptake of current Arctic atmospheric and sea ice observations in NPS is limited due to remaining challenges in coupled modelling, data assimilation and ensemble techniques, and observation usage (including their quality assessment). Further developing the NPS to overcome these challenges is key for enhancing observation uptake, and ultimately for further improving prediction.

* What cost-effective strategies can be used to reliably monitor the large-scale Arctic climate variability from in situ observations alone?
<figure><img src="image_7.png" /></figure>

A few well-placed bottom-moored buoys could allow us to reliably monitor the large-scale Arctic sea-ice volume variability. The cost of investing into, placing and recovering these buoys has not to be underestimated. Numerical models can provide guidance for optimising the spatial distribution of these buoys to retain a maximum of sea-ice volume variability.

* *What is the importance of the currently available Arctic observing systems for predictive skill in the Arctic and beyond from hours to years ahead?*

Existing Arctic observing systems have positive and complementary impacts on predictive skill from hours to seasons ahead; with conventional observations playing a dominant role during winter and microwave satellite observations playing a dominant role during summer (in systems with a high level of maturity in terms of the use of these data in polar regions) for short- to medium-range skill; and sea-ice thickness for example playing an important role in predictive skill at sub-seasonal to seasonal time ranges.

* *Are Arctic observations more beneficial for skill during certain types of weather regimes, as has been found for midlatitudes?*

Similar to what was previously found for midlatitudes, Arctic observations are more beneficial for predictive skill in midlatitudes for certain weather regimes (i.e. Scandinavian blocking).

* *What new Arctic observations would we need to further enhance predictive skill in the Arctic and beyond?*

Predictive skill can both be enhanced by adding new types of Arctic observations (e.g. snow and sea ice surface temperatures) and by ensuring the continuity of existing observing systems, and by improving their exploitation in NPS.

A clear message of this work is that investment in observing systems must be carried out synergistically with the investment in NPS. Investments to further improve all key components of prediction systems (coupled modelling, use of observations, data assimilation, and ensemble prediction techniques) may be more important than investments in observations themselves, in particular in polar regions where the specific challenges posed in each of these aspects are larger than in other parts of the globe. These challenges limit the extent to which observations can contribute to creating accurate initial conditions for weather forecasts, as well as an accurate, consistent and comprehensive depiction of past conditions through long-term reanalyses. For example, improving the use of microwave observations, which is currently suboptimal over snow and sea ice, would benefit not only weather forecasts, but also future reanalyses such as

those produced by the Copernicus Climate Change Service (ECMWF ReAnalysis fifth generation, ERA5; Copernicus Arctic Regional ReAnalysis, CARRA) for time periods as far back as 1979, when the first microwave sounding instrument was launched.

The quality of predictions and monitoring at high latitudes also critically depends on ensuring that all cryosphere relevant parameters and – in particular the sea ice, which has experienced and will continue to experience dramatic changes in areal coverage, thickness, extent and age – are monitored as continuously as possible and that no data gaps occur due to discontinued or postponed satellite missions. Ensuring a maximum of diversity in the type of retrievals (e.g. altimetry and passive microwave from satellites; airborne remote sensing; moorings – anchored buoys on the ice or even on the ocean floor; upward looking sonars) is key to avoiding the unfortunate possibility of data gaps that would be highly detrimental to the realisation of predictions. The availability of various observational datasets is also an advantage when it comes to independent evaluation of NPS output like sea ice reanalyses (Chevallier et al., 2017), which currently are very poorly constrained for sea-ice thickness.

Moreover, models and observations should form a “healthy ecosystem” where cross-fertilisation enriches and drives the development of each effort. An increased collaboration between the observation and modelling communities is thus strongly encouraged as models benefit from observations and vice versa. Adopting a routine, model-based perspective on the monitoring and optimisation of observing systems is urgently required to ensure that the data that will be collected in future observational campaigns or through enhanced observing systems are used optimally in numerical prediction systems, and hence will result in maximised predictive skill.

Strengthening efforts towards the convergence of weather and climate modelling capabilities, and more generally resource and expertise sharing as well as a reduction in the duplication of efforts is essential. Finally, it is also important to continue to (financially) support coordination and scientific underpinning of community efforts such as those led by the WMO WWRP Polar Prediction Project, which are instrumental for defining the scientific challenges and priorities and for channelling efforts for increasing predictive skill in polar regions and beyond.

## ACKNOWLEDGEMENTS

This study was supported by the APPLICATE project (727862), which was funded by the European Union's Horizon 2020 research and innovation programme. It was also supported by the Norwegian Research Council project no. 280573 "Advanced models and weather prediction in the Arctic: enhanced capacity from observations and
polar process representations (ALERTNESS)". This is a
contribution to the Year of Polar Prediction (YOPP), a
flagship activity of the Polar Prediction Project (PPP), initi-
ated by the World Weather Research Programme (WWRP)
of the World Meteorological Organization (WMO). We
acknowledge the WMO WWRP for its role in co-ordinating
this international research activity. The authors would
also like to thank Alexander Cress for performing the
observing system experiments at the German Weather Ser-
vice (DWD), Martin Künsting for designing Figure 1; and
John Methven, Jun Inoue and two anonymous reviewers
for providing comments on the manuscript.

AUTHOR CONTRIBUTIONS

**Irina Sandu** ID: Conceptualization; writing – original draft; writing – review and editing. **François Massonnet**: Conceptualization; writing – original draft; writing – review and editing. **Guillian Van Achter**: Writing – original draft; writing – review and editing. **Juan Acosta Navarro**: Writing – original draft; writing – review and editing. **Gabriele Arduini**: Writing – original draft; writing – review and editing. **Peter Bauer**: Writing – original draft; writing – review and editing. **Ed Blockley**: Writing – original draft; writing – review and editing. **Niels Bormann**: Writing – original draft; writing – review and editing. **Matthieu Chevallier**: Writing – original draft; writing – review and editing. **Jonathan Day**: Writing – original draft; writing – review and editing. **Thierry Fichefet**: Writing – original draft; writing – review and editing. **Daniela Flocco**: Writing – original draft; writing – review and editing. **Thomas Jung**: Writing – original draft; writing – review and editing. **Ed Hawkins**: Writing – original draft; writing – review and editing. **Stephane Laroche**: Writing – original draft; writing – review and editing. **Heather Lawrence**: Writing – original draft; writing – review and editing. **Jørn Kristiansen**: Writing – original draft; writing – review and editing. **Eduardo Moreno-Chamarro**: Writing – original draft; writing – review and editing. **Pablo Ortega**: Writing – original draft; writing – review and editing. **Emmanuel Poan**: Writing – original draft; writing – review and editing. **Leandro Ponsoni**: Writing – original draft; writing – review and editing. **Roger Randriamampianina**: Writing – original draft; writing – review and editing.

ORCID

Irina Sandu ID https://orcid.org/0000-0002-1215-3288

François Massonnet ID https://orcid.org/0000-0002-4697-5781

Gabriele Arduini ID https://orcid.org/0000-0002-6564-1699

Stephane Laroche ID https://orcid.org/0000-0002-4886-5535

Eduardo Moreno-Chamarro ID https://orcid.org/0000-0002-7931-5149

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**How to cite this article:** (2021) The potential of numerical prediction systems to support the design of Arctic observing systems: Insights from the APPLICATE and YOPP projects. *Quarterly Journal of the Royal Meteorological Society*, 147(741), 3863–3877. Available from: https://doi.org/10.1002/ qj.4182