Meet the scientists of the APPLICATE project.
Ed Blockley leads the Polar Climate Group within the Met Office Hadley Centre which focuses on understanding climate change in polar regions and development of the sea ice model component of the Met Office prediction systems. Furthermore, he is leading the UK’s Global Sea Ice (GSI) configuration managed by the UK's Joint Weather and Climate Research Program (JWCRP). Together with Gunilla Svensson, Ed is co-leading work package 2 of the APPLICATE project which aims to improve the representation of Arctic weather and climate in numerical models.
1. What is your working group “Polar Climate Group” working on, can you give us an example of a research question you address?
The Polar Climate Group at the Met Office Hadley Centre aims to improve our understanding of the climate in polar regions, with a particular emphasis on sea ice, the ocean, and the wider cryosphere.
One of the things that we are currently working on is trying to understand drivers of change in Arctic sea ice by performing a comparison of Arctic sea ice mass budget evolution in the latest round of climate model simulations.
Our coordination of this activity is being undertaken within APPLICATE and, under the auspices of the Sea Ice Model Inter-comparison Project (SIMIP), the study has been extended to include several international partners across the globe.
2. What are the Global Sea Ice (GSI) configurations and how are they used to predict the climate of the polar regions?
As well as trying to improve our understanding of the climate in polar regions, my group are also in charge of sea ice model development and evaluation at the Met Office.
In the UK we have a Joint Weather and Climate Research Programme under which climate model configurations are developed jointly by the Met Office and the Natural Environment Research Council (NERC). The Global Sea Ice (GSI) configurations are developed under a framework known as the Joint Marine Modelling Programme. These GSI configurations are used within several Met Office operational prediction systems, such as the GloSea seasonal prediction system, our coupled short-range weather forecasting system, and the FOAM operational ocean-sea ice analysis and forecasting system.
As well as being used for operational forecasting applications, these GSI configurations are also used as the sea ice component within the core UK climate models. This includes the HadGEM3 physical climate model and the UK Earth System Model (UKESM), climate simulations from which form the UK’s contributions to phase 6 of the Coupled Model Inter-comparison Project (CMIP6).
3. What do you think will be the main contribution of the APPLICATE project in advancing our understanding of changing Arctic sea-ice conditions?
APPLICATE is making some key steps towards advancing our understanding of how sea ice – and the change we are seeing in Arctic sea ice – interacts with the wider Earth system.
Within APPLICATE WP2 several groups have shown how important it is to take proper account of the sea ice, and overlying snow, within the atmosphere-ice-ocean coupling exchanges, which can have an impact on weather forecasts within the Arctic and beyond.
As part of APPLICATE WP4 we have shown, here at the Met Office, that the thickness of sea ice used to initialise seasonal forecasts can have a large impact on the evolution of sea ice through the summer. Better initialisation of sea ice can lead to large improvements in the simulated sea ice cover, which in turn can lead to local improvements in near-surface forecasts.
Finally, the PAMIP experimental protocol developed under APPLICATE WP3 will provide a very useful resource for improving our understanding of how changes in Arctic sea ice could impact the weather and climate in lower latitude areas such as Western Europe.
François Massonnet is working as a F.R.S.-FNRS Research Associate at the Université Catholique de Louvain (UCLouvain). His research focuses on the use and assessment of climate general circulation models for prediction at time scales from months to decades. Together with Irina Sandu, he co-leads work package 4 of the APPLICATE project. The work package aims to use state-of-the-art numerical weather prediction systems and climate models to guide the future development of Arctic observing systems over the next decade. The work package addresses two broad questions: (1) How can we make better use of the existing observational Arctic data in order to improve sub-seasonal to seasonal predictions at high- and mid-latitudes? and (2) How would new, hypothetical observations enhance the skill of predictions further?
1. How do climate general circulation models (GCM) work and how are they evaluated/validated?
Answering the first question is rather straightforward but answering the second one is rather complicated.
To cut a long story short, GCMs are basically a translation of our understanding of how the climate works, in a programming language. GCMs embody the basic laws of physics (conservation of mass, momentum and energy) that are applied to the relevant components: the ocean, the atmosphere, the cryosphere. Since a few years, GCMs have also attempted to simulate biogeochemical cycles, which is you could read the term Earth System Model (ESM). State-of-the-art GCMs cannot run on simple computers though: they feature at least millions of lines of code, need massive amounts of memory, and require significant storage space to host their results. Running GCMs on even the world's best supercomputers is a challenge! Because computational resources are finite, most GCMs cannot resolve all physical processes especially those occuring at the small scale (e.g., cloud convection). Yet such small-scale processes can have a large-scale influence. The way these small-scale processes are taken into account is one significant source of uncertainty in GCM-based projections.
Regarding evaluation, things become more complicated because evaluation inevitably implies, at some point, subjective choices. While climate scientists won't generally agree on how a GCM should be evaluated, they would probably agree on the general purpose of GCM evaluation. The goal of evaluation is not to assess whether a GCM is good or bad in an absolute sense (we know it can't be a realistic representation of all aspects of nature), but rather to assess whether a GCM can be useful in a particular context. But even so, how do you do in practice? It is necessary to give oneself a reference (verification dataset), but these references are tainted with errors. Then, one needs to summarize the behavior of the GCM using simple diagnostics (maps, time series, ...), which are certainly easy to visualize, but that have come at the price of loosing potentially useful information. Finally, one needs to perform statistical analyses to measure the agreement with the reference dataset; but plenty of measures exist and it is often possible to find one that works better than the others.
My experience with GCM evaluation is that we should be very careful not to over-interpret our results. I'm quite strict on this, personally. When I'm using a model to study a particular scientific question, I always start with the prior hypothesis that the model is not suitable for answering my question and try to convince myself otherwise by using available evidence. If the GCM proves not bad enough to be discarded, I use it as additional evidence to make my point.
2. How did you contribute to the 5th assessment report of the International Panel on Climate Change (IPCC)?
I had the chance to be involved a contributing author of the Chapter 12 of the IPCC Working Group 1 Assessment Report 5, dealing with long-term projections. I was not expected to work on this during my thesis, but this came as a fantastic opportunity. Together with my then-supervisor Thierry Fichefet and with a great team of other scientists, we spent days, if not months, shaping up the sea ice section of the Chapter. This involved reading dozens of papers, carefully choosing the wording in each sentence and making sure the contents reflects the actual state of knowledge. In this IPCC report, the summer Arctic sea ice projections got particular attention. I had introduced a method for attempting to reduce uncertainty in summer sea ice extent projections based on the current model performance, and this method was chosen to narrow down uncertainties in sea ice projections. We came to the conclusion that the Arctic could be summer ice-free by mid-century, with possibility of earlier instances if internal climate variability enhances the forced contribution to the negative trend.
I would recommend this or any other experience with IPCC, to anyone. The IPCC is seeking for reviewers to comment on the successive drafts of reports, so it's everyone's chance to get involved. The IPCC is criticized by some to be a non-transparent politically-oriented institution. I think by contrast that the IPCC adheres to very strict internal rules that guarantee a maximal of transparency and a balanced and cirtical assessment of the most recent literature. The best way to convince oneself about it is to be involved in the process, be it as author or as reviewer.
3. What do you think will be the main contribution of the APPLICATE project in advancing our understanding of changing Arctic sea-ice conditions?
The project has already delivered very concrete findings and recommendations, like a concerted protocol to evaluate models, the importance of using Arctic observations for weather prediction at lower latitudes and the fact that the available observational network could be better exploited for prediction purposes. Regarding sea ice, I would like to emphasize a recent result obtained by Ed Blockley (MetOffice) and Drew Peterson. They showed that the assimilation of sea ice thickness information drastically improved the seasonal prediction skill of summer sea ice at the spatial scale. This is very encouraging, are new sea ice thickness products are becoming available (e.g., ICESAT-2). Another important finding about sea ice in APPLICATE is the existence of very few degrees of freedom in the sea ice thickness field: that is, the typical time- and length scales of variability of thickness are large (~several months and several hundreds of km at least, respectively, according to modern reanalyses). What does that mean? If the real world has similar scales, then only a few point measurements would be enough to describe the thickness variability with enough accuracy. Potentially, this means that placing a few stations or moorings at strategic locations could already bring interesting insights on the state of sea ice.
Irina Sandu is leading the Physical Process Team at the European Centre for Medium Range Weather Forecasts (ECMWF). Her work seeks to improve the representation of turbulence and of surface drag and its impacts on the large-scale atmospheric circulation in the Integrated Forecasting System of ECMWF. Irina co-leads work package 4 of the APPLICATE project together with Francois Massonet (UCL). The work package aims to use state-of-the-art numerical weather prediction systems and climate models to guide the future development of Arctic observing systems over the next decade. The work package addresses two broad questions: (1) How can we make better use of the existing observational Arctic data in order to improve sub-seasonal to seasonal predictions at high- and mid-latitudes? and (2) How would new, hypothetical observations enhance the skill of predictions further?
1. What kind of numerical models do you work with and how good are they in the Arctic?
In the first part of my career I used a range of numerical models to answer questions related to a variety of physical processes such as clouds, turbulent mixing or radiative transfer. For example, I used Large Eddy Simulations, with a resolution of tens of metres, to understand what are the factors controlling the evolution of marine boundary layer clouds, and in particular the transition from stratocumulus to cumulus layer over the subtropical oceans. Since joining ECMWF over 9 years ago, I have been working with ECMWF’s global Integrated Forecast System, which is used for producing operational global weather predictions on timescales ranging from a few days to a few months ahead. My main focus is on medium-range weather forecasts, that is forecasts 3 to 10 days ahead. Medium-range weather forecasts in the Arctic have improved in recent decades following the general trend of skill evolution of global numerical weather prediction systems, of approximately one day per decade (Bauer et al., 2015). This means that forecasts 6 days ahead are nowadays as good as forecasts 5 days ahead were 10 years ago, in certain metrics characterising the large-scale circulation. However, the skill of weather forecasts in the Arctic remains lower than in the mid-latitudes.
2. What are then the challenges that need to be tackled to improve weather forecasts in the Arctic?
What is important to keep in mind is that the improvements in weather forecasting over the last decades are due to steady advances in all key ingredients of numerical weather prediction systems: models, observations and their use through data assimilation methods to create the best possible conditions for the weather forecasts, as well as to significant advances in supercomputing. Therefore, if we want to improve predictions in the Arctic on timescales from a few hours to seasons we need to invest work in three areas:
- enhanced coupled modelling of the atmosphere-snow-sea-ice-ocean system,
- data assimilation methods and
- the effective use of observations in the numerical weather prediction systems.
Arctic regions pose specific challenges for each of these three areas because for example model errors are large, in-situ observations are sparse, and satellite observations are difficult to use in data assimilation because of ambiguous signal properties, despite large data volumes.
3. What do you think is the main contribution of APPLICATE to improving weather forecasts in the Arctic and beyond?
In my view the biggest strength of APPLICATE is that by design is trying to address challenges in all the areas highlighted above, and is not just focusing on one of these aspects (i.e. modelling). By the end of the project, I think we will have several success stories in various key aspects of the NWP systems. For example, our collective efforts demonstrated the need for a more realistic coupling at the snow/sea-ice/atmosphere interface, and work has been done in this direction. At ECMWF we focused on developing a multi-layer snow scheme instead of the bulk single layer snow scheme used at the moment in the Integrated Forecasting System (IFS). This would improve the representation of the snow-to-atmosphere coupling, which is one of the key elements for accurately forecasting near surface temperature (and its diurnal cycle) and snow evolution (Arduini et al, JAMES, 2019). In parallel, in WP4 that i co-lead with Francois there are a few success stories as well, related to Arctic observations and their use for improving the initial conditions for weather forecasts. We have for example learned that assimilating novel observations of sea-ice thickness could lead to better sea-ice cover predictions a few months ahead (Blockley et al., 2018). We also learned that both Arctic conventional and satellite atmospheric observations play a key role in improving the initial conditions of the weather forecasts in polar regions and beyond, but that their relative impacts depend on the season. The conventional observations play the largest role during wintertime, while the microwave radiances have the biggest impact in summer. This is partly due to the fact that we do not make the best possible use of satellite radiances over snow and sea ice. I won’t elaborate on the reasons here, but there is an interesting publication explaining these results which is just out (Lawrence et al, QJRMS, 2019). Although this understanding does not directly lead to better predictions, it does motivate us to further improve the use of satellite radiances over snow and sea-ice in the IFS in the longer term.
Marta Terrado and Dragana Bojovic
Marta Terrado and Dragana Bojovic are scientists at the Barcelona Supercomputing Center (BSC) in the Earth Sciences' Earth System Services Group. Their interdisciplinary research aims to demonstrate the value of climate forecasting services, atmospheric composition and weather forecasting for society and key sectors of the economy. Marta and Dragana work on improving coproduction of climate services by engaging with various stakeholders and providing a link between climate scientists and climate information users. They help co-development of services for renewable energy, urban development, insurance, health and agriculture. In the APPLICATE project, Marta and Dragana co-lead user engagement work by increasing the awareness of Arctic change impacts, improving communication, maximizing exposure of the science produced within the project, and assuring that feedback received from stakeholders is incorporated in the project through knowledge coproduction.
1. What are you currently working on with APPLICATE students?
We are currently participating in the APECS-APPLICATE-YOPP online course, where we involve students in the active development of case studies. In APPLICATE, we define case studies as particular extreme events of Arctic weather and climate with an impact on specific aspects of the society or the economy of Arctic regions and beyond (e.g. extreme rainfall causing landslides or a decrease in energy generation in Europe related to a historical low winter sea ice in the Arctic).
Students’ involvement in this activity consists in developing new case studies in group collaboration and presenting them in an online seminar at the end of the course. By bringing students on board, APPLICATE can expect to receive fresh ideas, opening new ways to case study development, or generally, communication and engagement with stakeholders. In turn, students are provided the opportunity to experience interdisciplinary team collaboration, gaining a better understanding of the real-world application of scientific results generated in APPLICATE. This process will also allow students to have a broader overview of the complexity of developing climate services that can eventually be used. Questions identified in the process will show the importance of co-producing these services together with stakeholders.
2. What is the main challenge you encountered working with stakeholders in the Arctic?
Taking into account the diversity and complexity of the rapidly transforming Arctic region, selecting a group of stakeholders that we can collaborate with was not an easy task. The APPLICATE User Group is composed of representatives from various stakeholder groups, such as local communities, businesses, and international organizations. Our aim is to keep this group balanced when it comes to geographical representation, gender, sectoral interests, or stakeholder type, and at the same time, be careful not to increase too much the number of participants.
A second challenge is to get stakeholders involved and keep interaction with them. For that, we need to come up with ever-changing ways of motivating continuous interaction, given that participation in discussions is on a voluntary basis. Some examples are regular sharing of new project products, such as case studies, with members of the User Group, or meeting them at different events. We also invite members to the project general meeting, which was very well accepted by both the stakeholders and the project climate scientists, who otherwise do not have often opportunity to interact with users. Finally, stakeholders sometimes require information or services which are out of the scope or capacity of APPLICATE. Close collaboration between the user engagement team and project climate scientists is needed to fine-tune the project outputs and address user needs to the extent possible.
3. What do you think will be the main contribution of the APPLICATE project in advancing our understanding of how changing Arctic conditions affect local communities?
APPLICATE provides examples of the application of scientific findings to real-life situations in the Arctic and beyond. Examples are namely presented in our case studies. Another example is the Polar Prediction Matters Blog where stakeholders from the Arctic share with blog readers their experience from living and working in extreme Arctic conditions and provide new insights into what weather and climate information is currently used and what is still missing. Interaction with Arctic stakeholders is also helping the APPLICATE project identify and prioritize research gaps, moving forward our exploration and understanding of the northernmost corner of the Planet.
Rym Msadek is a CNRS research scientist at the European Center for Research and advanced Training in Scientific Computation (CERFACS). Her research focuses on the role of the ocean and sea ice in climate variability and predictability on time scales of seasons to decades.
She is especially interested in understanding the mechanisms of decadal variability in the ocean and determine how they can modulate the atmospheric response to anthropogenic forcing.
Within the APPLICATE project, Rym works on Northern Hemisphere atmospheric and oceanic response to Arctic summer sea ice loss using the CNRM-CM6 climate model to identify mechanisms and impacts.
1. What is the role of the ocean in the climate system and how does ocean circulation affect climate variability?
The ocean plays an important role in the climate system. It absorbs most of solar radiation in the tropics and counteracts the uneven distribution of solar radiation that reaches the Earth’s surface by distributing heat and moisture around the globe. The heat and moisture that is released by the ocean to the atmosphere contributes to shaping the weather systems. Without currents in the ocean, the temperatures over most regions would be more extreme, very warm at the equator and very cold at the poles, meaning less hospitable for humans and ecosystems.
2. What is the main signature of decadal variability in the ocean and why is this variability important to regulate European climate?
The ocean temperatures vary on multiple time scales, from few months to several decades. On decadal time scales, these variations can yield temperature anomalies at the sea surface that can last up to 60 years in the Atlantic basin, 20-30 years in the Pacific basin. This means that the Atlantic Ocean can be warmer or colder than usual for several decades leading to climate anomalies over the surrounding continents as well as in remote regions. For instance, studies based on observations and models showed that when the Atlantic is warmer than usual for several decades, temperatures over Western and Southern Europe are warmer by up to 0.5-1ºC and precipitation can diminish in particular during summer. This decadal variability is mainly driven by oceanic and atmospheric processes that are associated with natural variability of the climate system. However recent research indicates that external forcing (greenhouse gases, aerosols, volcanoes) can play an important role in modulating this variability. Understanding the mechanisms of this decadal variability is important as it could partially mask or amplify the global warming signal in the future. For this type of studies, modeling is particularly important because of the relative lack of long-term observations.
3. What do you think will be the main contribution of the APPLICATE project in advancing our understanding of how changing Arctic climate conditions will affect mid-latitudes?
There has been a lot of work before the APPLICATE project dedicated to the so-called linkages between the Arctic and the mid-latitudes, to determine whether sea ice loss can affect weather and climate away from the Arctic regions. The novelty of APPLICATE is to design experiments that are coordinated, which means that the modeling groups that will participate will follow the same protocol. This is very important because it will allow us to say with more confidence what are the impacts of Arctic sea ice loss that are robust and identify those that are still uncertain. Also, we will be able to say what type of weather changes we can expect from sea ice loss in the future, for instance, stronger winds during most winters over mid-latitudes, or more extreme temperatures over Europe or Asia. Another important contribution of the APPLICATE project is to identify the role of the ocean in the short-term response (few months) to sea ice loss and in the longer-term response (several decades) to sea ice loss. The first results that have been presented so far by few partners are very exciting and I’m looking forward to seeing more as we go further in the project.