it's still theoretical' [START_REF] St | Coastal Cities Wrestling With 'Managed Retreat' Ramifications Of Rising Sea Levels[END_REF]. As residents affected by Hurricane Sandy know and as Rebecca Elliott's book reminds us, the impacts of climate change are not merely theoretical. As the floodwaters lap at doorsteps in Florida and wildfires tear through suburbs in California, it has become clear that cities must change or continue to face destruction. The question is-in the face of enormous loss, who bears responsibility for this change? In Underwater: Loss, Flood Insurance, and the Moral Economy of Climate Change in the United States, Rebecca Elliott's singular achievement is creating a sociological framework for confronting this question.

The book is organized around the concept of loss, specifically losses that stem from climate change. Elliott argues that since 'it is obvious by now that climate change devastates, damages, and destroys' (Elliott, 2021b, p. 3), it is past time that social scientists turn their attention to the institution meant to manage these losses-insurance. It is through insurance that climate risks are assessed and rendered economic, or how floods and fires come to be economized as a matter of household finance. To illustrate this dynamic, Elliott studies the moral economy of climate change through an in-depth historical and sociological examination of flood insurance in the USA. Her empirical focus is the National Flood Insurance Program (NFIP).

The main product of Underwater is a politics of loss that hits close to home. The book begins with a story about a resident from Queens. Although his walls still bore the marks of flooding from Sandy, Palmer Doyle's main concern was whether he could afford the new flood insurance rates. While Doyle's home is no longer physically underwater, it could be financially so. Elliott's central argument is that insurance is a site through which loss is negotiated and moral dilemmas subsequently arise (Elliott, 2021b, p. 7). Negotiations include questions of what is fair and valuable, along with political and moral questions about who can live where, for how long, and at what cost. For Elliott, the moral economy of climate change can be conceptualized as three interrelated sets of dilemmas around loss: responsibility, justification and compensation for loss. These dilemmas arise and overlap in multiple chapters, illustrating the complex nature of moral economy.

The empirical story of the book is organized chronologically around floodplain past, present and possible futures. In Chapter 1, Elliott provides an historic overview of the origins of the NFIP and dilemmas it has faced with responsibility for loss. Elliott describes how flooding became constructed as a risk through insurance and how this transformation entailed 'specific visions of virtuous behavior' to reduce risk (Elliott, 2021b, p. 28). When addressing the impact that subsidized rates might have on long-term floodplain land use, policymakers and federal officials argued on the assumption that after homes had been damaged beyond repair, they would be abandoned, causing a 'natural attrition' of subsidized policies (Elliott, 2021b, p. 56). When this attrition did not materialize, the narrative justification for reauthorizing the NFIP in 1973 shifted to the deservingness of flood victims to access financial protection. In Chapter 2, Elliott takes the reader into the homes of New Yorkers dealing with the aftermath of Hurricane Sandy, where loss of a property meant loss of a home and social ties to place. For a woman faced with the decision to pay $100 000 to mitigate flood risk or send her children to college, flood insurance puts more than financial value at risk (Elliott, 2021b, p. 83). Homeowners challenge the assumption that financial compensation is a fair tradeoff for everything they will lose.

Even while Elliott describes the moral economy of loss in detail, she never lets out of sight that insurance is both an institution and a technology. This puts Underwater in conversation with recent literature addressing how technical efforts to account for climate change generate moral and political disputes over social distribution (Elliott, 2021a;[START_REF] Gray | Hazardous Simulations: Pricing Climate Risk in US Coastal Insurance Markets[END_REF].

Chapters 3 and 4 engage with the present-day stakes of technical efforts to identify and price risk and political efforts to reform the NFIP. After Hurricane Sandy, the Federal Emergency Management Agency's (FEMA) updated flood maps were meant to be an objective measure of elevated flood risk to New York City. However, when interviewing city planners and homeowners, Elliott found that commissioning these maps involved deeply complex social, political, and economic considerations. At stake in FEMA's new maps was not simply accurate knowledge of flooding risk, but rather how real risk informed by updated maps might cause insurance premiums to soar.

Reforming the NFIP generated yet another set of dilemmas around justification and responsibility for loss. Chapter 4 traces the backlash to the Biggert-Waters Flood Insurance Reform Act of 2012. The bill intended to restore financial solvency to the NFIP by expanding and updating risk-based rates. Opposition to Biggert-Waters, which would have eliminated many subsidies, was based on claims of deservingness and individual choice. The group Stop FEMA Now argued that because the NFIP had not properly communicated risk, floodplain homeowners were not able to exercise choice or individual control over their flood risk (Elliott, 2021b, p. 148). Elliott argues that in the end, reforming the NFIP became a debate about social solidarity and interdependence. Chapter 5 lends the analytical tools developed throughout the book to take stock of emerging trends in climate-related economic losses. Considering future projected losses, how should insurance aim to govern human decision-making? Ultimately, Elliott argues that insurance can play a variety of roles in 'social struggles over floodplain futures' (Elliott, 2021b, p. 188).

With rigor and moving detail, Elliott describes the empirical problem of insurance in a climate-changed world. Whether or not coastal cities like Del Mar adopt managed retreat will come down to a myriad of local factors, one of which may be flood insurance. What Elliott has shown is that because of climate change, these decisions exist within a politics of loss continually increasing in scope. From this perspective, Elliott leads an exciting new frontier for questions of risk and uncertainty, where recent scholarship in economic sociology has found that insurance influences behaviors at the individual and municipal level to encourage adaptation (Elliott, 2021a;[START_REF] Collier | Governing Urban Resilience: Insurance and the Problematization of Climate Change[END_REF]. It is a timely and important piece of scholarship, and a must-read for early and seasoned scholars of socio-economics alike.

Underwater: a cultural comment on solidarity in insurance programs against climate change Ste ´phanie Barral *

INRAE, France

*Correspondence: stephanie.barral@inrae.fr Elliott's recent book Underwater is undoubtedly a breakthrough in the social studies of environment and climate change. While the financial consequences of climate-led disasters, as well as the economic costs of conservation and mitigation policies are on the rise, economic sociology scholars have belatedly addressed this issue [START_REF] Gray | A (Rapid) Climate Audit of Economic Sociology[END_REF] and Underwater fills this gap in many ways. What is striking is how Elliott holds together both a story of institutions, rules, expertise, calculations and a story of ordinary people embedded in these flood insurance policies with their emotions, fear and strategies. Carving out the concept of moral economies, Elliott succeeds in addressing not only the everyday reality of people living in flood zones but also that of government officials and experts whose visions of flood risks and future bear normative commitments underlying the scientific orientations of the National Flood Insurance Program (NFIP). This is an important point as to some extent, the focus of environmental sociology seems mostly either oriented toward understanding the institutionalized answers to climate and environmental issues (see [START_REF] Mazmanian | Toward Sustainable Communities: Transition and Transformations in Environmental Policy[END_REF] as an illustration), or focusing on ordinary people, collective action and alternative ways of life (see, for instance, [START_REF] Mitchell | Dancing at Armageddon: Survivalism and Chaos in Modern Times[END_REF]. A great share of scholarly attention currently concentrates on institutional initiatives to transition economies, and to repair the damages done to the environment through mitigation policies. Economic sociology still poorly addresses the huge economic consequences of climate disruption on human activities and the way societies need to adapt to these catastrophes. On this note, thinking about 'loss' rather than 'repair' sheds light on the massive adaptation that societies carry out and will need to carry out in front of rising economic damages accruing from climatic events, an important analytical stance as natural catastrophes are on the rise.

Taking a historical perspective, Underwater explores what solidarity is in a changing biophysical environment, and Elliott is particularly interested in how this expression of solidarity may evolve with increasing threats on inhabited flood zones. Influenced by a Callonian conception of economy, she scrutinizes the individualization of risk management stemming from the federal program: calculated exposure to risk builds a system of responsibilities according to which homeowners have to manage their own risk while government and private industries are involved in diverse activities (studies, mapping, management and administration). Within this public-private institutional structure, Elliott seeks to understand how 'nature and distribution of responsibilities can change and shift over time, as a result of disputes between people and groups over the terms, uses and effects of the economization' (p. 5).

As a European reader of the book, I also see in the story of the NFIP a cultural reading that speaks of the American-ness of the story: beyond attention paid to the historical changes and adaptation, one can also see some constancy in the repartition of responsibilities within the structure of the insurance program. Yet the specificity of the case is not merely, as one would argue, about the American dream being based on the possibility to purchase a piece of land, expect value growth and transmit it to the descendants. Such propriety-based individualized conceptions of security also exist in countries where social states have provided mutualized safety nets for decades. There, access to land or homeownership also remains a desired horizon, especially in times when social protections are deregulated. What seems to be more distinctive is the way insurance systems are built to secure these property assets, and the way their organizational structures define the repartition of risks and responsibilities. I will give some evidence of these differences and reflect on their cultural meaning, though they would deserve further empirical research. Doing so highlights the usefulness of cultural comparison as it helps grasping the specificities of responsibility systems and collective action in addressing climate disruption, a necessary step for their adaptation to current and future challenges.

The NFIP is a State-led private-like insurance program that cannot drop any member. Throughout its history, the mutualization of premiums and risks has been carried out through the definition of flood zone landowners as final payers, as well as public dollars and taxpayers as the final reinsurance mechanism to be leveraged in case of economic imbalance of the program. What evolves with the new maps is the ratio between collective and individual costs, a key parameter through which regulators approach insurance systems. Reversely, the problem of who ought to bear the costs, collectively or individually, is not in question: the NFIP integrates a conception of flood risk management within which inhabitants of risky areas remain the mere category of citizens individually bearing responsibility for losses and damages.

Opening up insurance system into a more collective one can be though through the definition of insured persons. In European countries such as France, insurance systems against natural disasters are made compulsory for the entire population, regardless of individual exposure to risks. All contributions feed into mutual funds that are used in case of hazards. There solidarity in front of environmental hazards entails undifferentiated individual as part of a whole social system that covers the entire homeowner population. Yet, while responsibility for climate disruption is shared and diffuse, in the USA the strength of individual owner's responsibility seems to be heavily grounded which limits the possibility to question and reform it.

Beyond an extension of insurance system toward broader integration of individual homeowners, Underwater provides some insights about other options for the redesign of frontiers for flood risks responsibilities. Throughout the story, NFIP appears as a post-disaster management instrument that also bears a promise of regulating construction in flood zone. Yet it has always failed to do so, notably because no construction stakeholders have ever bore any responsibility in the insurance system. In Chapter 4, Elliott highlights how the insurance policy was meant to go hand in hand with a regulation of the construction sector but this has always failed (p. 162) 'With Biggert-Waters, Congress . . . provided resources to strengthen enforcement of land-use requirements and building codes. These measures address important and persistent implementation challenges, but it remains to be seen whether they can withstand the enduring pressure for local growth and development that have compromised such efforts in the past.' What we see here is how a local collective responsibility for construction in risky areas is translated into an individual responsibility that lays on the shoulders of the 'final consumer' of the construction sector, i.e. the landowner: the strength of the insurance policy lies at last resort on private ownership and property, in a context where construction is hardly regulated. This situation seems hardly resolvable without expanding the frontiers of responsibility to an intermediary level of local actors that would comprise of municipalities and real estate companies.

The recent reform of the Common Agricultural Policy (2023/2027) has enhanced the possibilities to develop a private insurance market for climate and economic risks for farmers, based on 'mutualization funds' to allow the pooling of risk management and the integration of agricultural organizations and industries for instance [START_REF] Barral | Risk Management within the Common Agricultural Policy. Promises of Data, Finance and Management in Face of Rising Hazards[END_REF]. More generally, this also points to the idea that bringing together insights from different cases (flood insurance for landowners, climate insurance for economic actors such as farmers) might be relevant to push further reflections on how, in systems where responsibilities are diffuse, they come to be segmented and more or less individualized, and how individual or collective propriety can play a role. This paves the way for much-needed future research.

Once things had settled a bit they both nudged me to use Sandy as part of a dissertation project. Surely, they said, such a massive disruption could provide insights into various topics of sociological interest. Since I was already thinking about housing, one of these advisors suggested I head to the Rockaways (once the subway was back online) where the flooding had been especially terrible. I could, he said, write a brilliant dissertation on housing costs post-disaster. I demurred and ultimately ignored the advice, believing that the sociology of disaster was too theoretically undercooked and that climate change was too illdefined for there to be a good sociology of it. It seemed to me that rising temperatures were abstractions that did not do much to motivate action. And I was not sure that Sandy (or any hurricane) could be a good case from which make inferences about the economics of housing.

How wrong I was! And there is no better evidence of my shortsightedness than Underwater, Rebecca Elliott's book that does things I did not think were possible. The book manages to be about one disaster, Hurricane Sandy, while also being about a product that is foundational to modern economies, insurance. The result is a contribution to understanding risk in an era where risk only seems to be growing exponentially. Indeed, as rising temperatures increase the likelihood of severe weather which, in turn, means more loss of homes to floods and fires, Underwater feels prescient.

This prescience is predicated on knowledge of the past, and the history and development of the National Flood Insurance Program (NFIP) laid out in the book reveals how (mostly) benevolent bureaucrats and politicians were quite attuned to loss. Flooding was a somewhat common occurrence in the USA in the early-and mid-20th century-enough of a problem that leaders saw some political benefit to coming up with a fix. But it is telling that the NFIP is finally established in 1968, a moment where Keynesian policymaking has perhaps passed its zenith and the neoliberal turn is not too far off. Insurance is used in lieu of more radical market interventions like strictly limiting development. (While the book alludes to it, I was waiting for a deeper critique of unfettered suburbanization which places more people in risky geographies and, by spreading jobs and homes apart, produces more carbon in the atmosphere. Bourgeois utopias are ecological hellscapes.)

The NFIP was initially designed to slowly submerge individuals into the market for risk. Policies were heavily subsidized early on to encourage uptake. The idea was to raise rates to reflect assessed risk over time after households in vulnerable places had bought in. But overrun rivers and record disaster costs reveal multiple problems with the logic behind the NFIP's design. The first is the aforementioned constantly rising vulnerability created by climate change [START_REF] Rhodes | Soaking the Middle Class: Suburban Inequality and Recovery from Disaster[END_REF]. The second is in collective conceptions of fairness and the redistribution of risk away from the state and to individual households [START_REF] Hacker | The Great Risk Shift[END_REF]. The fault, Elliott finds, is not in our storms but in ourselves.

In particular, the subsidization of flood insurance since its inception means that any changes to rates and premiums to bring them more in line with predicted risks is experienced by property owners as an attack on multiple levels-on their finances certainly but also on their way of life. This is why one of Elliott's interviewees, a homeowner who flooded during Sandy, describes rate adjustments as 'scarier than another storm'. And who could disagree with angry homeowners who have not been alerted to their risk before and now face the prospect of higher insurance costs and the potential that their main asset will decline in value if it is deemed to be at higher risk. Inherent in the analysis is a critical take on the prominent role individually owned real property now plays in guaranteeing a good retirement. In the contemporary USA, houses represent a great deal of exchange value and the commodification of something so central to everyday life is bound to pit individual interests against collective good while at the same time prompt opportunity hoarding [START_REF] Logan | Urban Fortunes: The Political Economy of Place[END_REF][START_REF] Besbris | Upsold: Real Estate Agents, Prices, and Neighborhood Inequality[END_REF].

Underwater reveals that the NFIP will forever be in the red. At the same time, it will never be able to effectively communicate risk. As Elliott points out, insurance, because it seeks to influence individual-level decision-making, is destined to fail as a solution to a collective, massive problem like climate change. Instead, we need a total overhaul of the economy. We need to couple mass decarbonization efforts with moves to decommodify housing. But if we build new green social housing, will those currently living in risky places come? Even if we made mobility away from vulnerable neighborhoods financially feasible and frictionless, we'd still be asking people to move out their homes-the thing that grounds daily life, gives them a sense of place and identity, and connects them to important networks of support. This does not mean we should not try. Turning away from insurance and the individualization of risk toward large-scale economic planning and social housing policy seems extremely necessary.

If I have any questions for Underwater they are about the deeper roots of valuation and the future of inequality. This is not to say that book is not concerned with inequality. Indeed, its development of loss as a sociological concept is useful and extremely humane given that climate change creates losses of so many kinds: lost land, lost homes, lost equity, lost communities, and so on. But Underwater does not address what kinds of places are deemed more or less valuable, and therefore more or less insurable, to begin with. Race is a central component how value is distributed across neighborhoods [START_REF] Besbris | Value Fluidity and Value Anchoring: Race, Intermediaries, and Valuation in Two Housing Markets[END_REF] and in the distribution of insurance [START_REF] Zhang | Planning for Housing Recovery? Lessons Learned from Hurricane Andrew[END_REF]. Was race not infused into the development of the NFIP to begin with? If the NFIP remains a major tool for managing risk in the USA, will it not further exacerbate racial inequalities in residential mobility, place attainment and wealth? Yet the fact that Underwater even raises these questions further demonstrates my poor sociological judgment back in the fall of 2012. With a magnificent book, Elliott has shown that no matter our empirical objects or theoretical dispositions, we must all be thinking about climate change. Not doing so puts us at risk of becoming scientifically and politically washed up.

The sociology of climate change and the politics of loss

Caleb Scoville * Tufts University, USA *Correspondence: caleb.scoville@tufts.edu

The title of Rebecca Elliott's Underwater is a hydrologic-economic double entendre. Floodwaters may inundate a house, but the financial consequences of such storms-or the anticipated risks thereof-are dislocating in a sense that is less physical, but no less real. Rather than focusing on 'moments of acute and conspicuous destruction and loss' exemplified by neighborhoods being flooded, Elliott draws our attention to 'the before and after', and specifically 'the institution that manages loss and losses, inscribing disasters into the everyday reality of residents, communities, and governments, even in times of relative calm: insurance' (Elliott, 2021, p. 3).

As Elliott shows, flood insurance has both linking and containing functions. First, linking. Flood insurance 'links loss, lives, and landscapes'. It does so by way of economization. Hazards and decisions are 'rendered economic, formatted in ways that make things calculable and people calculative' (Elliott, 2021, pp. 3-4). Physical changes, societal patterns of settlement and development, and individual choices about where to live, which structures to fortify, and how, are drawn together through insurance rate maps and premiums. These devices objectify risks that are being amplified by climate change.

When an insurance rate map is drawn or redrawn, a homeowner may face the choice of either paying a radically higher premium or undergoing an expensive flood-proofing process. Given that homes' resale values are also impacted by how they are zoned in insurance rate maps, relocating to higher and dryer ground may prove even more economically difficult after reclassification, complicating attempts to align economic incentives with flood risk. While flood damages themselves cause tremendous losses, policies seeking to proactively manage flood risk can put peoples' homes on the line, even financially underwater. In a society in which rising real estate values have been a counterweight to wage stagnation, it is understandable why some of Elliott's interlocutors in the field found new flood maps to be 'scarier than another storm' (Elliott, 2021, p. 193).

That was linking. Now for containing. Flood insurance as conceived in the USA's National Flood Insurance Program (NFIP) is an institutional container for flood risk that locates it in the individual household. The household is subjectivized as a specific kind of calculative and moral agent. The question of climate change preparedness is posed, not so much in terms of collective determination, but individual responsibility.

Yet this container is imperfectly sealed. This is where a second hydrologic metaphor becomes relevant: the overflow, a term that Elliott borrows from Michel Callon. Much as physical containers can only carry so much and lose their ability to hold their contents when put under pressure, losses associated with floods and climate change overflow 'the narrow bounds of calculation framed by flood insurance' (Elliott, 2021, p. 99). A powerful example that Elliott threads artfully through the book is a movement called 'Stop FEMA Now', which 'took its grievances to Congress, [earning] the solidarity of some of the most progressive and conservative senators and representatives' (Elliott, 2021, p. 205). The costly effects of instituting new flood risk maps overflowed the individualizing calculative rationality imposed on the household, sparking a mobilization of homeowners facing steep increases in premiums. This overflow laid bare the contradictions inherent in the collectively induced but locally emplaced financial burdens associated with climate change.

Elliott conceptualizes Stop FEMA Now as an accidental climate public. 'These publics', Elliott writes, 'unite people who may or may not care about climate change per se, who may be otherwise at different ends of the political spectrum, but who share what it is they have to lose in a world of rising seas, devastating storms, shifting precipitation patterns, and widespread wildfires' (Elliott, 2021, p. 205). While the politics of loss exemplified by Stop FEMA Now overflowed the narrow policy vision embodied in the NFIP, it remained shaped by its institutional origin in homeownership. For instance, it excluded renters because they do not own the kinds of assets that the movement sought to defend.

Through this analysis, Elliott ventures to expand the notion of climate politics beyond action that is self-consciously organized around climate change. Underwater sets the agenda for this urgent task. Yet as a way of engaging this agenda, I think it is worth considering how the book's empirical focus on homeownership, and policy attempts to contain climate risk therein, provides an image of one species of the politics of loss among others.

Consider how different the politics of loss can look in the context of failing public infrastructure. By way of illustration, here is an excerpt from a news story that appeared the year that Underwater was published:

Rising tensions in the Klamath Basin could come to a boil soon, as two Klamath Project farmers plan to breach the fenced headgates of the federal irrigation project's main canal and try to release water, likely triggering a standoff with the federal government. [. . .] They are staffing a large canvas tent with volunteers from the local branch of People's Rights, a national organization formed in 2020 by militant activist Ammon Bundy, and they're trying to rally support. "I'm planning on getting D.C.'s attention [. . .] We're going to turn on the water and have a standoff." Federal officials announced last month that, due to extremely low water levels in Upper Klamath Lake, they would retain what water there is to protect endangered fish species. That means no water will be released this season from the lake into the "A" Canal, the main channel that delivers irrigation water to the Project. [The farmers] say they plan to enter the fenced area surrounding the headgates, which is topped with barbed wire and posted with keep-out signs threatening criminal charges. [START_REF] Dillemuth | Irrigators Say They Plan to Force Open Klamath Headgates and Release Water[END_REF] At a certain level, this story bears a family resemblance to the one Elliott tells. Farmers find solidarity in climate-induced losses associated in this case, not with a flood, but a drought. Yet whereas in the case of Stop FEMA Now, the individualizing logic of homeownership overflowed into a collective and nonpartisan mobilization targeting institutional politics, the Klamath farmers-members of a constituency that owes its very existence to federal water infrastructure-defended their interests through anti-government vigilantism justified by appeals to individual rights. Cases like this highlight an aspect of the politics of loss that is less visible in the empirical context of Underwater. That is, the politics of loss not only creates and reinforces solidarities that, however imperfectly, evince our mutual dependency. It can also exacerbate societal divisions, consolidating senses, not only of us but also of them.

We've had other glimpses of what this looks like recently in the USA. Shocking losses of life and property caused by the 2021 ice storms in Texas were blamed incorrectly on renewable energy by right-wing political and media elites. Those lies were strategic ploys to blunt a progressive climate politics. In the words of Fox News's Tucker Carlson, 'a reckless reliance on windmills is the cause of this disaster.' Republican Texas Governor Greg Abbott followed up claiming that 'this shows how the Green New Deal would be a deadly deal for the United States of America' (quoted in [START_REF] Rupar | Fox News turns Winter Storm Uri into a cudgel to own the libs[END_REF]. Similarly, in my own research on water politics in California, I analyze how a legally protected fish called the Delta Smelt has been mobilized by right-wing demagogues. The controversy has become decoupled from the actual flows of water because its small size and lack of charisma has made the species an ideal symbolic vessel for the message that an imagined 'they'-smug liberal environmentalists in faraway cities-care more about 'their stupid little fish', as it was dubbed by a member of Congress on the House floor, than 'ordinary people like you' [START_REF] Scoville | A "Stupid Little Fish": Science, Law and the Politics of Environmental Decline in California[END_REF]. In such cases, we can see how real losses associated with climate change, whether disasters or resource depletion, can be perversely exploited by denialists and entrenched interests to undermine the very solidarity we need to tackle those problems.

Thus, the challenge of the politics of loss turns not only on finding ways to overcome the individualizing tendencies of our current political and economic context, as is the emphasis of Underwater, but also making sure that what we collectively stand to lose is not eclipsed by the affective divisions that dominate so much of contemporary politics. That the book does not explore this particular political obstacle is not a flaw. Great books open up fields of inquiry beyond their empirical context, and with Underwater, Elliott has done just that. Not only does the book show how to place climate change at the center of sociological analysis by tracing its overflows into disparate aspects of our social lives. It will be a valuable resource for anyone who shares the goal of building a climate politics that 'recognizes our fates as connected' (Elliott, 2021, p. 213).


Esteban Alonso-González
email: esteban.alonso-gonzalez@univ-tlse3.fr

Simon Gascoin

Sara Arioli

Ghislain Picard

Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment

Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much higher spatiotemporal resolution in the coming years.

To investigate the value of TIR data to improve SWE estimation, we developed a synthetic data assimilation (DA) experiment at five snow-dominated sites covering a latitudinal gradient in the Northern Hemisphere. We generated synthetic true LST and SWE series by forcing an energy balance snowpack model with the ERA5-Land reanalysis. We used this synthetic true LST to recover the synthetic true SWE from a degraded version of ERA5-Land. We defined different observation scenarios to emulate the revisiting times of Landsat 8 (16 d) and the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TR-ISHNA) (3 d) while accounting for cloud cover. We replicated the experiments 100 times at each experimental site to assess the robustness of the assimilation process with respect to cloud cover under both revisiting scenarios. We performed the assimilation using two different approaches: a sequential scheme (particle filter) and a smoother (particle batch smoother).

The results show that LST DA using the smoother reduced the normalized root mean square error (nRMSE) of the SWE simulations from 61 % (open loop) to 17 % and 13 % for 16 d revisit and 3 d revisit respectively in the absence of clouds. We found similar but higher nRMSE values by removing observations due to cloud cover but with a substantial increase in the standard deviation of the nRMSE of the replicates, highlighting the importance of revisiting times in the stability of the assimilation performance. The smoother largely outperformed the particle filter algorithm, suggesting that the capability of a smoother to propagate the information along the season is key to exploit LST information for snow modelling. Finally, we have compared the benefit of assimilating LST with synthetic observations of fractional snow cover area (FSCA). LST DA performed better than FSCA DA in all the study sites, suggesting that the information provided by LST is not limited to the duration of the snow season. These results suggest that the LST data assimilation has an underappreciated potential to improve snowpack simulations and highlight the value of upcoming TIR missions to advance snow hydrology.

Introduction

The seasonal snowpack plays a key role in many ecological and hydrological processes worldwide (Barnett et al., 2005). Due to its high albedo and insulating capabilities, the extensive snow-covered area of the Northern Hemisphere influences the Earth climate system (Henderson et al., 2018). In Published by Copernicus Publications on behalf of the European Geosciences Union. mountain regions, the seasonal snowpack is also an important source of runoff during the summer when the water demand peaks. Hence, accurate knowledge of the snowpack conditions has an important economic value (Sturm et al., 2017). The snow cover is also a source of natural hazards such as floods caused by rain-on-snow events or snow avalanches, events that are expected to increase due to the impacts of climate warming [START_REF] Ballesteros-Cánovas | Climate warming enhances snow avalanche risk in the Western Himalayas[END_REF]Musselman et al., 2018).

Despite its importance, monitoring the snowpack remains challenging for both scientists and water management agencies. The variable nature of the snowpack makes it challenging to deploy and maintain dense enough ground-based monitoring networks (Kinar and Pomeroy, 2015). Therefore, snow hydrologists have developed methods to take advantage of satellite remote sensing since the beginning of the Landsat programme [START_REF] Rango | Application of a Snowmelt-Runoff Model Using Landsat Data[END_REF]). Yet, the application of remote sensing in snow hydrology remains hindered by the lack of direct observations of the snow water equivalent (SWE) in mountain regions [START_REF] Dozier | Estimating the spatial distribution of snow water equivalent in the world's mountains[END_REF].

Numerical models allow for simulating many snowpack state variables, including the SWE. However, their accuracy is primarily constrained by the large uncertainty in the meteorological forcing (Raleigh et al., 2015). Recent studies have suggested that data assimilation (DA) of remotely sensed products is the path forward to estimate the spatial distribution of relevant snowpack characteristics (Aalstad et al., 2018;Charrois et al., 2016;Cortés and Margulis, 2017;[START_REF] Margulis | A landsatera Sierra Nevada snow reanalysis (1985-2015[END_REF][START_REF] Smyth | Improving SWE Estimation With Data Assimilation: The Influence of Snow Depth Observation Timing and Uncertainty[END_REF]Stigter et al., 2017). Using DA techniques it is possible to fuse model simulations and multiple remote sensing datasets to improve the snowpack simulations. In particular, the snow cover area has been assimilated in many case studies due to its widespread availability (e.g. Baba et al., 2018;Alonso-González et al., 2021). Yet, the extent of the snow cover provides no direct information on the internal state of the snowpack and is blind to snowpack changes when the pixel is fully snow covered [START_REF] De Lannoy | Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado[END_REF].

The ice surface temperature (IST) is a key state variable for simulating the snowpack evolution. Physically based snowpack models solve the energy balance equation iteratively along the time dimension, estimating the IST at each time step of the model (Essery, 2015;[START_REF] Liston | A distributed snow-evolution modeling system (snowmodel)[END_REF]. It is also a key parameter to estimate the emitted energy as outgoing long-wave radiation. The estimation of the IST allows for detecting the occurrence of surface melting events, as when the IST reaches 0 • C all the added energy is converted to melt. Thus, assimilating IST may provide key information about the timing of the melting events. Also, the assimilation of the land surface temperature (LST) (i.e. the temperature of the Earth surface independently of if it is snow covered) may improve the snowpack simulations by different mechanisms. The IST is physically bound to the melting point temperature, while once the snow melts, the LST can exceed 0 • C. Thus the assimilation of the LST may indirectly provide in-formation about the snow cover area too. Also, it should be possible to improve the snow simulations by retrieving thermal information when there is no sunlight, like during the nighttime or during the polar night at high latitudes. A previous study has shown that IST DA could potentially improve the surface ice mass balance simulations of the Greenland ice sheet (Navari et al., 2016), fusing synthetic IST estimations with the CROCUS snow model. On the other hand, previous research has suggested few improvements in the SWE simulations after assimilating LST simulations retrieved from the Meteosat Second Generation (MSG) (∼ 6 km spatial resolution) in the Alps (Piazzi et al., 2019). However, the coarse resolution of the LST products of MSG prevents its use in complex terrain. Also, the rapid variation in LST at hourly timescales can make it difficult to assimilate this variable using particle filters as done by Piazzi et al. (2018) and could be addressed using different algorithms. Thus, more research is needed to assess the potential of high-resolution LST DA.

The thermal imagery already available only offers coarse resolution for the snow applications over complex terrain (MODIS, Sentinel-3) or long revisiting times (Landsat). This has probably prevented the study of the impact of LST DA, although recent research has suggested that LST can provide useful information to retrieve internal snowpack properties [START_REF] Colombo | Introducing Thermal Inertia for Monitoring Snowmelt Processes With Remote Sensing[END_REF], a capability that can be exploited from satellites [START_REF] Colombo | Mapping snow density through ther-mal inertia observations[END_REF]. The availability of high-spatiotemporal-resolution LST products will be improved in the short term with the appearance of new satellites, such as the French-Indian mission Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) [START_REF] Lagouarde | The Indian-French Trishna Mission: Earth Observation in the Thermal Infrared with High Spatio-Temporal Resolution[END_REF]. TRISHNA is expected to provide surface temperature measurements at 60 m spatial resolutions every 3 d at the Equator, with an increasing revisiting time towards the poles. Also, given the agenda of the space agencies, high-resolution thermal infrared retrievals will be readily accessible in the near future, including the Copernicus Land Surface Temperature Monitoring (LSTM) [START_REF] Koetz | High Spatio-Temporal Resolution Land Surface Temperature Mission -a Copernicus Candidate Mission in Support of Agricultural Monitoring[END_REF], which will offer similar observations to TRISHNA but with improved spectral, spatial and temporal resolutions, and the Surface Biology and Geology (SBG) satellite [START_REF] Cawse-Nicholson | NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms[END_REF] from NASA, which will provide similar high-resolution thermal infrared (TIR) images of the surface of the Earth. The combination of these three missions may eventually provide close to bi-daily (day-and nighttime) high-resolution thermal infrared observations of the Earth surface. In this context the objectives of this work are (i) to test the potential of the LST to improve the snowpack simulations and (ii) to explore the effect of increasing the temporal resolution of the observations.

A convenient approach to emulate future remote sensing observations is to use an observing system simulation experiment (OSSE). For example, Navari et al. (2016) assessed the feasibility of integrating ice surface temperatures in a regional climate model estimate of the Greenland ice sheet sur- face mass balance through an OSSE. Synthetic experiments were also used to explore the potential of data assimilation techniques in improving the snowpack simulations [START_REF] Clark | Assimilation of snow covered area information into hydrologic and land-surface models[END_REF][START_REF] Revuelto | Assimilation of surface reflectance in snow simulations: Impact on bulk snow variables[END_REF]Smyth et al., 2019).

Here, we designed an OSSE to evaluate the benefit of future remote sensing LST to simulate seasonal SWE. In this experiment, synthetic LST and SWE data were generated in several climatic regions. Synthetic LST data were assimilated into a snowpack model under different cloud cover scenarios and with different satellite revisit times. The benefit of assimilating LST was studied by comparing the posterior (after assimilation) SWE to the synthetic SWE.

2 Data and methods

Synthetic observations

We selected five sites in snow-dominated regions of Europe, spanning 40 • latitude from the Pyrenees mountains to the Svalbard archipelago. The sites were chosen approximately every 10 • latitude to sample different climatic influences. The southern sites (Gerlachovský štít, Bigorre) are located in mountain regions (Tatra, Pyrenees), Finse is located on an elevated plateau (Hardangervidda), and Tromsø and Ny-Ålesund are in the polar circle. Gerlachovský štít is in eastern Europe, and its climate is influenced by its continental characteristics; Tromsø and Ny-Ålesund exhibit obvious polar climates, and Bigorre shows a montane climate with Mediterranean influences.

We used ERA5-Land surface reanalysis data (∼ 9 km spatial resolution) (Muñoz-Sabater et al., 2021) to force the Flexible Snow Model (FSM2) (Essery, 2015) over 4 consecutive hydrological years from 1 September 2017 to 31 August 2021. From this simulation, we retrieved the SWE and LST time series, which were used as the synthetic truth. The LST was exported at 13:00 local time, corresponding to the foreseen TRISHNA overpass time. To mimic observational noise, we added to the LST time series a Gaussian noise with a mean of 0 and a standard deviation of 1.5 K. This standard deviation was chosen as an intermediate value between the reported root mean square error (RMSE) obtained by the comparison of Landsat 8 with in situ measurements of the snow surface temperature (RMSE = 2.0 K) (Robledano et al., 2022) and the expected performance of LST products delivered by the TRISHNA mission.

The synthetic true LST time series were downsampled with a period of 16 and 3 d to emulate revisit times of Landsat 8 and TRISHNA respectively. We simulated the impact of cloud cover on data availability by further removing values in the synthetic LST time series at random dates selected from a uniform distribution. We defined four different cloud cover scenarios with probabilities of 0 %, 25 %, 50 % and 75 %. Following the same strategy we generated synthetic fractional snow cover area (FSCA) true observations to be assimilated. The synthetic FSCA observations were degraded by adding random Gaussian noise with a mean of 0 and a standard deviation of 0.17 based on [START_REF] Aalstad | Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography[END_REF].

For each site, we created a new degraded meteorological forcing to run FSM2. First we averaged the ERA5-Land data from the nearest nine cells (i.e. resampling the spatial resolution from 10 to 30 km approximately). Then, we added autocorrelated Brownian noise in 12 h time windows (the data assimilation window of ERA5) using the standard deviation of the variable itself (Supplement Fig. S1). We further perturbed the precipitation field after aggregation, dividing the precipitation by 2. This strong perturbation was chosen to emulate precipitation biases that are typically found in global reanalyses and large-scale precipitation products (Beck et al., 2019), potentially leading to a large underestimation of SWE in mountain regions [START_REF] Wrzesien | Characterizing Biases in Mountain Snow Accumulation From Global Data Sets[END_REF]. A similar value has already been used in previous snow data assimilation experiments [START_REF] Deschamps-Berger | Improving the Spatial Distribution of Snow Cover Simulations by Assimilation of Satellite Stereoscopic Imagery[END_REF].

Data assimilation experiments

The degraded meteorological forcing and synthetic LST were used to feed the Multiple Snow Data Assimilation System (MuSA). MuSA is an open-source ensemble-based data assimilation toolbox built around FSM2 (Alonso- [START_REF] Alonso-González | ealonsogzl/MuSA: v1.0 GMD submission[END_REF]. We used the same initial conditions to run FSM2 within MuSA (soil temperature profile, initial LST and absence of snow); therefore we did not perform a spin-up. To emulate the differences between the reality and modelling pipeline, we used a simplified parameterization scheme of FSM2 in MuSA (Table 2). The assimilation experiments were done using a particle batch smoother (PBS) [START_REF] Margulis | A particle batch smoother approach to snow water equivalent estimation[END_REF] and a particle filter (PF). Smoother algorithms are typically used to develop reanalyses, as all time series of information are available, whereas filtering is used for operational forecasting where future observations with respect to the analysis step are not available [START_REF] Largeron | Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review[END_REF]. A rigorous description of the algorithms, the underlying theory and implementation details can be found in [START_REF] Alonso-González | ealonsogzl/MuSA: v1.0 GMD submission[END_REF].

We performed a final experiment in which we assimilated fractional snow cover area (FSCA) using a PBS. This experiment has been performed using exactly the same set-up as https://doi.org/10.5194/tc-17-3329-2023

The Cryosphere, 17, 3329-3342, 2023 for the assimilation of LST. This includes running the simulations over the same geographical areas, using the same forcing and cloud cover scenarios, as well as using a different FSM2 parameterization to generate the synthetic truth and MuSA. Using exactly the same set-up allows for a simple comparison between the performance of assimilating LST and FSCA.

The prior ensemble of FSM2 simulations was composed of 300 particles that were generated by perturbing the air temperature (additive perturbation) and the precipitation (multiplicative perturbation). The perturbations were time invariant and were randomly drawn from a normal distribution of mean µ = 0 and standard deviation σ = 2 (temperature) and a log-normal distribution with mean µ = 0.45 and standard deviation σ = 0.8 of the underlying normal distribution (precipitation). These parameters were chosen to cover the expected differences between the "true" forcing and the degraded forcing (Fig. S2) and were obtained by preliminary trial-and-error tests. Both PF and PBS are very prone to collapse. Although there are uncertainties in other variables, keeping a limited number of dimensions helps to prevent the collapse of the ensembles. In addition, not correcting for the other variables introduces errors into the simulations, which also helps to prevent the collapse of the ensembles. The observation errors prescribed for the synthetic true observations were the same as those defined to generate the degradation Gaussian noise.

The PF performs the analysis sequentially, i.e. each time an observation occurs, and the PBS is a smoother; hence it assimilates all the available observations in a single time win-dow, propagating information from the observations forward and backward in time. Here the assimilation time window was defined as a hydrological year (i.e. one snow season). In both the PF and PBS, prior weights of ensemble members (particles) are updated based on the likelihood, i.e. a measure of the distance between the predictions of each particle and the observations. The posterior weights are then used to estimate posterior statistics from the ensemble, typically its weighted mean and weighted standard deviation. In the case of the PF, we used the bootstrap resampling algorithm to eliminate particles with low weights and to replicate particles with high weights by sampling with replacement randomly from the probability distribution of the updated weights. To prevent the filter from collapsing (all the weight is shared by a few particles and eventually just one particle), new perturbation parameters were drawn from a normal approximation of the posterior from the previous analysis step at each new analysis step, instead of resampling both the states of the model and the parameters.

Evaluation of the experiments

For each site and each cloud cover scenario, we ran MuSA and generated a posterior SWE. However, the output of MuSA is stochastic due to the random generation of the forcing perturbation parameters. Also, the position of the gaps in the different cloud cover scenarios and the Gaussian and Brownian noises added to the observations and forcing respectively are random. Therefore, to increase the statistical robustness of our results, we repeated each assimilation ex- periment 100 times, drawing new gaps and noise for each replicate of the experiment. This created an ensemble of posterior SWE, which was compared to the synthetic true SWE.

In total, for a given site, MuSA was run 2400 times (100 replicates ×4 cloud cover scenarios ×2 revisit times ×3 DA experiments). This corresponds to 720 000 FSM2 runs (300 particles by MuSA run), summing up to 3 600 000 FSM2 runs considering the five study areas, which is equivalent to simulating the snowpack over a period of 14 400 000 years. To compare the performance of data assimilation, in ev-ery site regardless of the absolute magnitude of the SWE, we used the normalized RMSE (nRMSE) as a performance score:

nRMSE = 1 n n i=1 (Pred i -Ref i ) 2
Ref ,

where n is the number of samples

Results

As expected, the degradation of the ERA5-Land meteorological forcing had a large impact on the open-loop SWE simulations (Figs. 2 and S3, S4 and S5). In comparison with the synthetic true SWE, the average normalized RMSE (nRMSE) was 61 % (after removing the summertime months of July and August). The degradation of the ERA5-Land data caused an overall reduction in the simulated SWE, leading to a shorter snow season at all sites (Fig. 2). However, the LST assimilation with the PBS substantially improved the SWE simulations (Fig. 2). This improvement was evident during both revisit times, although the 3 d revisit scenario (nRMSE = 13 %) outperformed the 16 d revisit scenario (nRMSE = 17 %).

The posterior SWE series in Fig. 2 were averaged from an ensemble of 100 replicates under clear-sky conditions. The uncertainty in the replicates is mostly caused by the different random noises in the observations (see Sect. 2 "Data and methods"). Figure 3 Figure 4 summarizes the results of the PBS from all experiments under every cloud cover and revisit scenario. In all cases, the data assimilation significantly reduces the nRMSE in comparison with the open-loop simulations. In most situations the nRMSE is always higher for the 16 d revisit compared to the 3 d revisit, but the difference was more pronounced under the 50 % and 75 % cloud cover scenarios. In addition, the standard deviation of the nRMSE of the 100 replicates is higher for the 16 d compared to the 3 d revisit scenarios. Both the averaged nRMSE and the standard deviations increase with the cloud cover, with an average nRMSE for all the sites ranging between 13 % and 16 % in the case of the 3 d revisit experiments and 17 % and 28 % for the 16 d revisit experiments. The results indicate a comparatively lower reduction in SWE nRMSE when FSCA is assimilated compared to LST assimilation ranging between 27 % and 39 % in the case of the 3 d revisit experiments and 41 % and 45 % for the 16 d revisit experiments (Fig. 5), being comparable to results obtained in previous studies assimilating real observations (Aalstad et al., 2018) and thus confirming the robust- ness of the OSSE design. Furthermore, the greater dispersion of nRMSE values suggests an increased sensitivity to cloud distribution in the case of FSCA compared to LST.

Figure 6 shows the distribution of the mean of the posterior precipitation perturbation parameters obtained from the 100 data assimilation runs using the PBS. It demonstrates that the assimilation of LST reduces the error in the precipitation forcing, since the posterior parameter distributions approximate the actual multiplicative perturbation factor of 2 to compensate for the 0.5 scaling factor used to degrade the input precipitation. However, the difference between the true precipitation and the degraded precipitation should not exactly be equal to the scaling factor of 2 due to the perturba-tion strategy used, the fact that we do not correct all forcing variables and the differences between the FSM2 configurations (Sect. 2) as well as the fact that other components of the forcing were also degraded and were not included in the simulation to induce errors in FSM2. As observed in Fig. 4, the standard deviation of the posterior perturbation parameters of the replicates increases when comparing the 3 d with the 16 d revisit scenarios and with the cloud cover probability.

Whereas the above results show that the PBS algorithm clearly improved the SWE simulation, it was not the case with the PF. Figure 7 summarizes the results of the same experiments shown in Fig. 4 but using the PF instead of the https://doi.org/10.5194/tc-17-3329-2023

The Cryosphere, 17, 3329-3342, 2023 PBS. In this case the improvement in the average nRMSE of the posterior simulations is not as obvious with respect to the open loop as in the PBS experiments. Although in the medium-latitude cases the nRMSE shows a moderate improvement compared with the open loop on average, several runs among the 100 replicates had a higher nRMSE than the open-loop run. The revisit or cloud cover scenarios had no clear effect on the nRMSE. The results in the high-latitude areas yielded a higher nRMSE standard deviation than in the medium-latitude regions. This is a consequence of the very cold conditions in these high-latitude areas, causing some particles to become glaciers due to the perturbed forcing (non-zero SWE at the end of the hydrological year).

Discussion

Navari et al. (2016) showed the potential of IST data assimilation to improve the surface mass balance of the Greenland ice sheet in a regional climate simulation with an ensemble batch smoother. Our study also suggests that the assimilation of LST can improve seasonal snow simulations in sites with different climate contexts. With the PBS, the improvement was substantial independently of the site; i.e. the climatic context did not exhibit an obvious influence on the results. However, our results with the PF also support the conclusions of Piazzi et al. (2019), who did not obtain obvious improvements in the posterior SWE simulations after assimilating LST using an ensemble Kalman filter. There- information than FSCA. In addition, the lower dispersion of nRMSE values suggests that LST may be more resilient to cloud cover distribution than FSCA. The information of the whole seasonal trajectory of the LST is propagated to the posterior by using a smoother but not by using a filter. This is highlighted at the high-latitude study areas, where the polar conditions made snowmelt impossible at the end of the hydrological year for some of the replicates (Fig. S2), leading to very high nRMSE using the PF (Fig. 7). These results suggest that the LST may be less beneficial to snowmelt forecasting applications, where the use of filters is more extended, to update the model as new observations arise, but it should be regarded as valuable information to improve snow reanalyses which aim to reconstruct snow cover climatologies.

Our results also suggest that even the currently available thermal infrared estimations of the LST from Landsat missions have the potential to significantly improve SWE simulations despite a revisit time of 16 d. The emulated revisiting times of both Landsat and TRISHNA are the expected values at the Equator and can be lower in other latitudes. Here we did not study the effect of the spatial resolution but hypothesized that high resolution (i.e. Landsat-like) is needed for snow cover simulations in the studied regions. Landsat TIR images have a 100 m resolution, which makes them suitable to sample the slope scale in mountain terrain, hence resulting in homogeneous conditions in the energy balance budget [START_REF] Baba | Effect of Digital Elevation Model Resolution on the Simulation of the Snow Cover Evolution in the High Atlas[END_REF]. Despite the low revisiting times of the Landsat mission, Landsat TIR imagery may be useful to im-prove SWE simulations using a smoother data assimilation algorithm, an approach that to our knowledge has not been explored yet. More research should be carried out on this topic, especially in the context of joint assimilation experiments where more than one variable is assimilated.

Nevertheless, the change in revisit from 3 to 16 d in our experiments translated into a higher posterior nRMSE. Therefore, we expect significant progress with TRISHNA observations, not to mention the enhanced spatial resolution (approximately 60 m). The benefit of the 3 d revisit was particularly evident under 50 % to 75 % cloud cover. This should be considered, as previous global estimates of the cloud cover suggest values closer to our highest-cloud-cover scenario [START_REF] Wylie | Trends in Global Cloud Cover in Two Decades of HIRS Observations[END_REF]. For instance, cloud cover probability in MODIS products reached 60 % in the Alps and 50 % in the Pyrenees (Gascoin et al., 2015;[START_REF] Parajka | Spatio-temporal combination of MODIS images -potential for snow cover mapping[END_REF].

In any case, under both revisit scenarios, the cloud cover decreased the precision of the replicates of the posterior SWE, i.e. the variability between repeated experiments, but the average was only marginally affected. In other words, the cloud cover reduced the robustness of the data assimilation, but even regions with a persistent cloud cover could benefit from LST assimilation. The different replicates of each experiment exhibited different results, with a variance that increased with the number of gaps introduced into the synthetic LST observations, suggesting that not all the combinations of observations are equally informative. This was also obvious regarding the posterior precipitation perturbation parameters, as the standard deviation of the different replicates increased with the percentage of cloud cover.

Despite the promising potential of the LST to improve SWE simulation, some limitations of the current study inherent to the synthetic nature of the OSSE should be taken into consideration. The synthetic nature of the experiment could lead to an overestimation of the value of LST assimilation. This effect is mitigated in our experiment as typically done in OSSEs by (i) the degradation of all forcing variables, while only temperature and precipitation were corrected, and (ii) the different FSM2 parameterizations used to create the synthetic truth. The simulation of the cloud cover scenarios was generated by selecting random dates from a uniform distribution. However, in some regions the cloud cover exhibits marked seasonal patterns [START_REF] Sudmanns | Assessing global Sentinel-2 coverage dynamics and data availability for operational Earth observation (EO) applications using the EO-Compass[END_REF] that may pose a challenge to updating the snowpack simulations even with smoothers if cloud cover is more frequent during key periods in the snow season. Also, the surface temperature observation in complex terrain may differ from the simulated temperature due to intra-pixel variability as a consequence of variable snow and/or mixed-pixel conditions (Robledano et al., 2022;[START_REF] Lundquist | Separating snow and forest temperatures with thermal infrared remote sensing[END_REF]. But this issue is greatly limited by the expected increase in resolution. In light of the results of this work, the next step is to conduct experiments using real remote sensing observations, although the general lack of data may complicate the interpretation of the results.

Conclusions

The motivation for the study of LST data assimilation is the upcoming launch of high-resolution thermal infrared spatial missions with an improved revisit time and resolution in the next few years. We implemented a synthetic data assimilation experiment to study the potential of LST in improving SWE simulations along a latitudinal gradient in the Northern Hemisphere. The methodology was based on the generation of synthetic LST estimations and true SWE estimates, as well as a prior ensemble of SWE simulations generated by forcing FSM2 with degraded meteorological fields. The MuSA snow data assimilation software was used to generate SWE posterior time series using the particle batch smoother and particle filter algorithms to be compared with synthetic true SWE.

The results suggest that the assimilation of LST has great potential to improve seasonal snowpack simulations across all the tested sites. Gap-free LST series improved the average nRMSE of the open-loop simulations from 61 % to 17 % and 13 % for the 16 d and 3 d revisiting times respectively. However, a lower revisit frequency caused an increase in the variance of the nRMSE when the runs were replicated 100 times, showing that the performance of the assimilation would depend on cloud cover scenarios. This conclusion was more evident with high-cloud-cover scenarios, highlighting the importance of the revisit time in thermal infrared remote sensing to reduce the uncertainty in the updated SWE.

The type of data assimilation was also key to explain the role of LST in improving SWE simulations. The particle batch smoother strongly improved the simulations, whereas the particle filter was much less effective and could even cause a degradation of the simulations. This effect could be interpreted as a consequence of the strong seasonal signal of the LST, which reflects the duration of the snow season. But the lower performance shown by the FSCA assimilation suggests that the LST assimilation provides more than just information on snow duration.

Overall, our results encourage a more systematic use of the current LST products within snow data assimilation studies, especially if the objective is to perform a snow reanalysis, which can benefit from observations acquired over an entire snow season.

Figure 1 .

 1 Figure 1. Main workflow of the OSSE.

Figure 2 .

 2 Figure 2. Comparison of hourly time series of synthetic true SWE with the open-loop simulations and the posterior SWE after assimilating LST with a revisit time of 16 or 3 d (0 % cloud cover scenario) using the PBS. Here, the posterior SWE is the average of the 100 replicates, and the shaded areas represent the 95th to 5th quantile range.

Figure 3 .

 3 Figure 3. Comparison of hourly time series of synthetic true SWE with the open-loop simulation, the 100 replicates of posterior SWE after assimilating LST with a revisit time of 3 d, varying cloud cover scenarios and the average of the experiments in Tromsø.

  shows every posterior SWE realization in the case of the Tromsø site under different cloud cover scenarios when assimilating LST at 3 d resolution. This figure shows that the spread of the replicates increased with the cloud cover probability. The standard deviation of the obtained nRMSE values over each cloud cover scenario ranged from 4 % to 10 % in this particular case.

Figure 4 .

 4 Figure 4. Normalized root mean square error (nRMSE) of the posterior SWE after assimilating LST using the PBS compared with the synthetic true SWE for each experiment. The bars indicate the mean nRMSE, while the error bars indicate the standard deviation in the 100 replicates.

Figure 5 .

 5 Figure 5. Normalized root mean square error (nRMSE) of the posterior SWE after assimilating FSCA using the PBS compared with the synthetic true SWE for each experiment. The bars indicate the mean nRMSE, while the error bars indicate the standard deviation in the 100 replicates.

Figure 6 .

 6 Figure 6. Boxplots showing the distribution of the posterior precipitation perturbation parameter for each experiment estimated by the PBS. The dashed line indicates the true perturbation that was applied to the forcing.

Figure 7 .

 7 Figure 7. Normalized root mean square error (nRMSE) of the posterior SWE after assimilating LST from the PF compared with the synthetic true SWE for each experiment. The bars indicate the mean nRMSE, while the error bars indicate the standard deviation in the 100 replicates.

Table 1 .

 1 Geographical coordinates and elevation (m a.s.l.) of the ERA5-Land centroids used in the study.

	Centroid	Longitude Latitude Elevation
	Ny-Ålesund (Norway)	12.0 •	78.9 •	124
	Tromsø (Norway)	19.8 •	69.5 •	647
	Finse (Norway)	07.5 •	60.6 •	147
	Gerlachovský štít	20.2 •	49.2 •	1479
	(Slovakia)
	Bigorre (France)	00.1 •	42.9 •	1843

Table 2 .

 2 FSM2 configuration chosen (and configuration number) to generate the synthetic truth and simulations.

	Process	Synthetic true FSM2	MuSA FSM2
	Snow albedo	(2) Diagnosed from snow age	(1) Diagnosed from LST
	Snow thermal conductivity (1) Estimated from density	(0) Constant thermal conductivity
	Snow density	(2) Viscous compaction	(0) Constant density
	Turbulent exchange	(1) Monin-Obukhov stability functions (0) Neutral stability
	Snow hydraulics	(2) Gravitational drainage	(1) Bucket storage
	Snow cover fraction	(2) Asymptotic function	(1) Linear function

TheCryosphere, 17, 3329-3342, 2023 https://doi.org/10.5194/tc-17-3329-2023

https://doi.org/10.5194/tc-17-3329-2023The Cryosphere, 17, 3329-3342, 2023

Code and data availability. The MuSA v1.0 code is available at https://github.com/ealonsogzl/MuSA/tree/v1.0 (last access: 8 August 2023; DOI: https://doi.org/10.5281/zenodo.7014570, Alonso-González, 2022). The original FSM2 code is found at https://github. com/RichardEssery/FSM2 (Essery, 2015) and in the MuSA repository with slight modifications to the original version. ERA5-Land data are available for download from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/, Copernicus Climate Data Store, 2023). https://doi.org/10.5194/tc-17-3329-2023 The Cryosphere, 17, 3329-3342, 2023

Supplement. The supplement related to this article is available online at: https://doi.org/10.5194/tc-17-3329-2023-supplement.

Author contributions. Conceptualization: EAG, SG. Data curation: EAG. Formal analysis: EAG, SG, SA, GP. Funding acquisition: EAG, SG. Investigation: EAG, SG. Methodology: EAG. Project administration: EAG, SG. Resources: EAG, SG. Software: EAG. Supervision: SG, GP. Validation: EAG. Visualization: EAG. Writing (original draft preparation): EAG. Writing (review and editing): EAG, SG, SA, GP.

Competing interests. The contact author has declared that none of the authors has any competing interests.

Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Financial support. This work was supported by the Centre National d'Études Spatiales (CNES) through the postdoctoral grant of Esteban Alonso-González, the PhD grant of Sara Arioli and the TOSCA programme.

Review statement. This paper was edited by Nora Helbig and reviewed by Bertrand Cluzet and one anonymous referee.


Dr Christophe Coudercy

Dr Mathieu Prevot

Dr Pavel Afanasiev

Dr Stéphane Loridant
email: stephane.loridant@ircelyon.univ-lyon1.fr

In situ generation of intimacy in MnOx+SAPO-34 mechanical mixture for the conversion of syngas to light olefins

Bifunctional MnOx+SAPO-34 catalysts were evaluated for the conversion of syngas to light olefins (OXZEO process). They were prepared, either by mechanical mixing of 100 µm-scale particles agglomerates, or by mixing of ethanol suspensions, containing homogeneously distributed micrometer-scale primary particles of oxide and zeolite. The selectivity to light olefins, initially lower for the mechanical mixture, reached the value of the suspension mixture after a 14 h on-stream period. This convergence was explained from SEM images by in situ breaking of SAPO-34 agglomerates present in the mechanical mixture and dispersion of SAPO-34 crystals among MnO x primary particles, decreasing the contact distance between the two catalytic functions. These results evidence the importance of intimacy at the micrometer scale to improve the catalytic properties in the OXZEO process.

Introduction

Light olefins (C2 = -C4 = ) are key building-block chemicals presently produced worldwide in huge amounts (market exceeding 320 Mt.y-1) [1] mostly by steam cracking and Fluid Catalytic Cracking (FCC). The increasing demand and the evolution of raw materials require the development of on-purpose, less energy-intensive processes. The OXZEO (Oxide-Zeolite) process is promising because it allows the selective and stable production of light olefins from syngas combining a hydrogenation catalyst to synthesize methanol or ketene and an acidic zeolite to convert the intermediate to light olefins. Since the pioneer paper of the X. Bao' group, [2] numerous other metal oxide-zeolite combinations yielding high C2 = -C4 = selectivity have been reported and this concept was recently reviewed. [3,4] We previously investigated the combination of MnO x and SAPO-34 zeolite in a mechanical mixture for the conversion of syngas to light olefins and evidenced that CH3OH is a key intermediate. [5] High selectivity to C2 = -C4 = would require optimal proximity determined by the local concentration of methanol [6] and related both to the kinetics of formation and consumption and its diffusion from the oxide to the zeolite. Furthermore, those olefins can be hydrogenated to the corresponding alkanes on the oxide. Therefore, the intimacy between the two catalysts may significantly influence the catalytic performance: as such, random mixtures of powders are usually more efficient than arrangements of catalytic beds. For the ZnCrO x -SAPO-34 and MnO x +SAPO-34 catalytic systems, it was shown that the reaction benefits from the decrease of distance between the catalytic functions due to reduced mass transport limitation. The effect was observed for granule size decreasing down to the micrometer. [7] Mortar mixing of ZnZrO x and SSZ-13 for 10 min led to improved catalytic properties. [8] However, it was reported that ball milling of mixtures of oxide and zeolite led to degradation of the zeolite properties. [9][10][11][12][13].

In this work, a mixture of MnO x nanoparticles and SAPO-34 crystals was prepared by combining suspensions of both solids to improve intimacy at the primary grain (nanometer) scale. We assumed that if two solids are dried separately and then mixed, many agglomerates of the same type of particles would persist in the mixture. On the contrary, mixing of liquid suspensions prior to drying would ensure lesser agglomeration of the same type particles and better intimacy as it have been shown for BaTiO 3 preparation. [14,15] This suspension mixture (SM) was then evaluated for the conversion of syngas to light olefins and its catalytic performances were compared with those of a mechanical mixture (MM) of dry powders MnO x +SAPO-34. The two mixtures were characterized after calcination, activation and reaction crossing different techniques, in particular Scanning Electron Microscopy. The goal was to establish a relationship between the textural and catalytic properties.

Results and Discussion

The XRD diffractogram of MnO x (Figure 1a) shows that it contained mostly MnO and a small amount of Mn 3 O 4 as crystalline phases. This pattern revealed that MnCO 3 precursor was decomposed into MnO during the thermal treatment at 430 °C under 20%H 2 -N 2 and then slightly oxidized upon passivation. The crystallite size of the MnO phase determined by the Scherrer equation was around 12 nm. The large width of the Mn 3 O 4 peaks suggests that this phase is present as a poorly crystallized layer over MnO.

TEM images of the prepared MnCO 3 precursor show well defined spherical porous nanoparticles with a mean diameter of 466±44 nm (Figures 2a andb). SEM image of MnO x (Figure 2c) shows that the morphology has been retained after thermal treatment and further reveals that the porous spheres are composed of smaller nanoparticles when the thermal treatment temperature remains below 900 °C, as reported in the literature. [16] The adsorption isotherms of the solids before and after thermal

   ◼ ◼ ◼ ◼ ◼ ◼ ◼ ◼ ◆ • • • • • ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ ◆ (d) Relative intensity (a.u) 2 (°) (a) (b) (c)  
treatment were of type V with H 2 hysteresis (Figure S1) [17][18][19] revealing the presence of mesoporosity in the solid. The mesopore diameter strongly increased from 8 to 24 nm upon thermal treatment (Figure S2). The BET surface area decreased after calcination from 103 to 53 m 2 .g -1 . The molar composition of SAPO-34 was Si/Al/P=0.15/1/0.68 ((Si+P)/Al=0.83). Its XRD diffractograms before and after thermal treatment (Figures 1c andd, respectively) mostly contain the peaks of the chabazite structure. They were close to the one indexed for the composition Si/Al/P:0.22/1/0.75 (PDF 47-0429 ICDD pattern). The slight shift and change of relative intensity of peaks upon thermal treatment were due to the removal of organic species during calcination, leading to slight modification of the crystalline structure. [20] However, the presence of small amounts of Al(OH) 3 crystalline phases was evidenced after drying (Figure 1c). The corresponding peaks were not observed after calcination suggesting that Al(OH) 3 was decomposed into amorphous Al 2 O 3 . TEM images agree with the XRD analysis since they show SAPO-34 crystals in the 100 to 300 nm size range (Figures 2d ande) as well as extra framework alumina (Figure 2f). The 27 Al NMR signals at 39, 10 and -12 ppm (figure S3a) were assigned to tetra, penta-and octacoordinated aluminum atoms, respectively. [21][22][23] The latter were formed by addition of two water molecules to tetrahedrally coordinated framework aluminum atoms or corresponded to atoms present in extra-framework Al 2 O 3 . The 31 P NMR spectrum of SAPO-34 (Figure S3b) revealed mainly a P(OAl) 4 local structure with a chemical shift of -27 ppm [21][22][23] and small amount of P(OAl)x(H2O)y hydrated local structures at -16 ppm. [22] The latter species were probably present at the outer surface of the crystals. [21,23] Only isolated Si atoms have been observed in the 29 Si NMR spectrum of SAPO-34, with a chemical shift of -89 ppm (Figure S3c). [21][22][23] The BET surface area of the prepared SAPO-34 was 581 m 2 .g -1 ; that of the microporous one was 524 m 2 .g -1 , the difference between the two values (57m 2 .g -1 ) corresponding probably to the intergranular porosity. The prepared SAPO-34 contained 894 mol.g -1 of weak acidic sites and 925 mol.g -1 of strong ones as probed by NH 3 -TPD (Figure S4).

The suspension mixture sample 90%MnO x +10%SAPO-34-SM was characterized by XRD after thermal treatment under 20%H 2 -N 2 . Its diffractogram (Figure 1b) corresponded to a combination of MnO x (also treated under 20%H 2 -N 2 , Figure 1a) and SAPO-34 after calcination (Figure 1d), showing that the phase composition of SM was similar to that of the MM. SEM image of the SM sample (Figures 3a andS5) shows SAPO-34 crystals highly dispersed among the MnO x nanoparticles. On the contrary, large aggregates (100-200 m) of either MnO x or SAPO-34 were observed for 90%MnO x +10%SAPO-34-MM, (Figures 3b andS6) with clear separation between them (Figure 3c) even if a small quantity of MnO x nanoparticles covers SAPO-34 aggregates. Hence, as expected, the intimacy between MnO x and SAPO-34 phases is higher for the SM sample than for the MM one. The catalytic properties of MnO x +SAPO-34-MM and MnO x +SAPO-34-SM mixtures were assessed to determine the impact of the difference of intimacy at the grain scale. Figure 4a compares the time evolution of the CO conversion: it was quite similar and close to 4% for the two mixtures. Similar temporal evolution was also observed for the selectivity sets of the two mixtures, with a small increase in CH 4 and C2 selectivity, while the C3 and C4 selectivity slightly decreased (Figure 4b). Note that as expected for a SAPO-34 zeolite, the C2-C4 selectivity was high (ca 55% after 14 h). Furthermore, as shown in Table 1, the selectivity values after 14 h were close even if a slightly lower C2-C3 selectivity was obtained for MnO x +SAPO-34-MM (49 vs 52%) to the benefit of the CH 4 one (12 vs 9%). This difference, more pronounced initially (Figure 4b), could be related to the difference of intimacy. Indeed, a lower proximity between the oxide and the zeolite grains should favor CH 3 OH hydrogenation to CH 4 . [5,8,9] Figure 4a shows that the olefin to paraffin O/P ratio was initially higher for the SM catalyst than for the MM one (~10.0 vs 5.8). Furthermore, the evolutions were different: the O/P ratio reaches its maximum after 4 h for the SM and after 6 h for the MM sample. The O/P maximum reached for the SM (18.7), is higher than for the MM (16.6). Then, the two curves converge toward a similar value of O/P ~ 16, after 14 h on stream.

Those results suggest that the catalytic properties of the two catalyst mixtures, initially different, converge over time on stream. SEM images of the MM sample were recorded right after activation under H2 flow. Figure 5a shows the presence of well-defined zeolite aggregates of ca 100-200 m size that are covered by MnO x primary particles (see also the zoom in Figure S7a). Conversely, only few SAPO-34 crystals were distinguished among the primary MnO x aggregates (Figure S7b). Hence, SEM analysis revealed that intimacy of the MnO x +SAPO-34-MM mixture after activation was similar to that of the fresh state, with well-separated aggregates of MnO x and SAPO-34 (Figure 6a).

SEM images were also recorded after 2 h on stream: large aggregates of zeolite are still visible in Figure 5b. However, they seem to begin to lose their shape as revealed by Figure 6b (in grey in Figure S8b): SAPO-34 crystals begin to separate from each other and the transition between zeolite and oxide domains is less sharp. Indeed, as shown in Figure S8a, more zeolite crystals are dispersed inside the MnO x domains. However, large domains of zeolite with small quantity of MnO x primary particles are still visible. Those images suggest that a textural evolution takes place under the reaction conditions on stream, improving the intimacy, and that it is a slow phenomenon.

Finally, SEM images were recorded after 14 h on stream for both mixtures. It appears that the intimacy of MnO x +SAPO-34-MM greatly improved with time on stream: no zeolite aggregates are observed anymore, but only a homogeneous powder is visible at the 10 µm scale, Figure 5c. Figure 6c and Figure S9 clearly show that the powder is composed of SAPO-34 crystals well dispersed among the MnO x agglomerates. Hence, a high intimacy was obtained for MnO x +SAPO-34-MM, similar to fresh MnO x +SAPO-34-SM. Note that the intimacy of this latter mixture does not evolve significantly under the reaction conditions: no specific aggregates could be identified while some SAPO-34 crystals were well dispersed in the MnO x powder (Figures 5d, 6d and S10). The early evolution of catalytic properties of OX-ZEO bifunctional catalysts is not really addressed in literature even if often noticeable. [2,8,[24][25][26] It could arise from equilibration of diffusion processes occurring in the zeolite during formation of the hydrocarbon pool. [START_REF] Nesterenko | Zeolites and Zeolite-Like Materials[END_REF] However, the olefin to paraffin O/P ratio was shown to strongly depend on the density of Brønsted acid sites (BAS) while the CO conversion is almost constant when the BAS density is high enough to convert CH 3 OH. [8,[START_REF] Wang | [END_REF][29][30] Therefore, the temporal increase in the O/P ratio could arise from a decrease in the BAS density while remaining high enough to keep the conversion constant. Therefore, the difference of evolution between MM and SM catalysts has to be explained by the evolution of another parameter. One could assume that Mn 2+ cations tend to migrate into the zeolite under the harsh reaction conditions of the OXZEO process as previously reported for other metals. [31] However, Ding et al. [7] who also studied MnO x +SAPO-34 mixtures reported that such migration was negligible. Furthermore, Mn-exchanged SAPO-34 prepared by us, was poorly active (X(CO)=1%, Figure S12), showing that the two active sites for the conversion of syngas into light olefins are definitely the MnO x surface and BAS of the SAPO-34 crystals.

It was previously reported that the distance between ZnZrO x oxide and zeolite does not influence the catalytic properties for the conversion of syngas to olefins, contrarily to syngas to aromatics process, because of fast diffusion of methanol. [32] In the present work, the temporal evolution of the O/P ratio could be partly related to the evolution of intimacy i.e. of the distance between the oxide and the zeolite. Indeed, its improvement probably decreases the diffusion path of methanol toward the zeolite and of the olefins outward it. However, the apparent syngas consumption rate is kinetically limited by the formation of methanol on the oxide and not by the diffusion of methanol inside the zeolite aggregates. Otherwise, the conversion should increase as a function of time on stream which is not observed experimentally. In a similar manner, because the oxide aggregates do not change in size under stream, the methanol diffusion flow inside of the oxide is not modified and the methane selectivity related to the oxide is almost not affected. On the other hand, the improvement of intimacy leads to a decrease in the contact time of the olefins with the zeolite. This helps to reduce the proportion of olefins that is converted into paraffins and hence to improve the O/P ratio. Ding et al. also observed an improvement of the O/P increasing proximity between MnO x and SAPO-34 samples with grains ranging from 900 to 200 µm. However, SAPO-34 aggregates kept their shape under stream unlike in this study. [7] The fundamental driving force of the improvement of intimacy of 90%MnO x +10%SAPO-34-MM is certainly entropy, that always favors a homogeneous mixture of (weakly interacting) particles over an inhomogeneous one. Apparently, soft agglomerates of zeolite are broken and redistributed under the reaction conditions. Then, the "driving forces" that provide input of energy and make the particles move are probably temperature and pressure (which allow to break the agglomerates) and microscopic fluctuations of gas flow (which allow displacing the particles). As a result, the static bed appears to be not-so-static, and this is one of the main points of our paper.

Conclusion

In this study, it was shown that the intimacy of a mechanical mixture composed of MnO x and SAPO-34 agglomerates improves during the first hours in reaction conditions. During this period, agglomerates of SAPO-34 crystals are broken and their dispersion occurs among MnO x primary particles. The increase of O/P ratio with time on stream is proposed to be partly related to this intimacy improvement. After 14 h, the intimacy reached was close to that obtained for a suspension mixture, leading to similar catalytic properties. It clearly shows that high intimacy at the micrometer scale is required to get high performances for the Ox-Zeo process and intimacy optimization is further needed.

Experimental Section

MnO x powder was prepared from MnCO 3 as follows: two 0.5 M aqueous solutions of (NH 4 ) 2 CO 3 (Alfa aesar, Ref 36229, NH3 30.0%) and MnSO 4 (Prolabo, Ref 25-301, 98%) were prepared. 5 ml of each solution were sampled and 6.35 ml of glycerol (Sigma-Aldrich, Ref G7893, 99.5%) were added to each of them and stirred until homogenization. The (NH 4 ) 2 CO 3 solution was added to the MnSO 4 solution under stirring and left at room temperature under stirring for 1 h. Upon addition of the carbonate solution to the sulphate solution, a white suspension was formed. After 1 h of stirring, the suspension was centrifuged (3500 rpm) and washed three times with water. The recovered MnCO 3 solid was dried at 100 °C overnight. Then the obtained powder was treated under 20%H 2 -N 2 flow of 100 mL. min -1 at 430 °C for 12 h to produce MnO particles. After the treatment, MnO was exposed to flowing air at room temperature to passivate the surface.

The preparation of SAPO-34 was carried out as follows: 16.8 g of Al-isopropoxide (Sigma Aldrich, 220418, ≥ 98%) and 39 ml of TEAOH (Alfa Aesar, 43023, 35 wt% in H2O) were mixed in a Teflon recipient for 1h30 at room temperature. After that, 0.74 g of silica (Evonik, Aerosil 200 153070413, ≥99.99%) was added to the mixture and mixed for 1 h. Finally, 9,4 g of H 3 PO 4 (Honeywell Fluka, 30417-1L, ≥85%) and 8,7 g of H 2 O were added to the mixture and stirred for 3 h at room temperature. Then the Teflon recipient was closed and placed in a stainless-steel autoclave which was heated to 100 °C for 12 h without stirring, for ageing. Finally, the autoclave was heated to 200 °C and maintained at this temperature for 48 h under autogenous pressure. After this step, the suspension was recovered and washed by water with repetitive centrifugation. The recovered solid was dried at 100 °C overnight and then calcined in air at 200 °C for 2 h (heating rate of 1 °C.min -1 ). Then, the temperature was increased slowly up to 550 °C with a ramp of 0.5 °C.min -1 and maintained for 6 h.

To prepare the SM-sample, 143 mg of calcined SAPO-34 was finely grinded, added to 5 ml of ethanol and stirred vigorously. The obtained suspension was then treated in an ultrasound bath for 15 min to favour dispersion of particles. A MnCO 3 suspension was prepared as previously described for MnO x . After washing, 1.700 g of MnCO 3 nanoparticles were re-dispersed in 50 ml of ethanol passed in an ultrasound bath for 15 min producing a suspension. Then, the two suspensions were mixed together and dried at 100 °C over day. It would lead after calcination to a mixture composed of 1.050 g of MnO x and 0.143 g of SAPO-34 (12% of zeolite). During the drying step, the suspension was periodically shacked at 30 min interval to ensure that no segregation between the SAPO-34 and MnCO 3 particles occurred. After drying, the obtained powder was treated thermally under 20%H 2 -N 2 at 430 °C for 12 h. The resulting solid is named 90%MnO x +10%SAPO-34-SM in the manuscript. By comparison, 90%MnO x +10%SAPO-34-MM was a mechanical mixture of 90 wt% of MnO x and 10 wt% of SAPO-34, separately sieved between 180 µm and 105 µm.

Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) has been used for quantification of Si, Al, P and Mn on an ICP-OES Activa spectrometer (Horiba). For Al, P and Mn quantification, the solid was firstly dissolved in H 2 SO 4 +HNO 3 +HF at boiling point before being injected into the analysis apparatus. For Si, the solid has been fused with lithium tetraborate into a Pt-Au crucible at 1100 °C. The obtained solid was then dissolved into a 20% HCl solution before injection.

X-ray diffractograms were obtained between 4 and 80° (2Θ) on a Bruker D8 Advance A25 diffractometer equipped with a Ni filter (Cu Kα radiation: 0.154184 nm) and a one-dimensional multistrip detector (Lynxeye, 192 channels on 2.95°). The counting time per point was 0.5 s. The International Center for Diffraction Data (ICDD) library was used for phase identification. Furthermore, the crystallite size was determined using the Scherrer equation: D=Kλ/(βi*cos(θ)) where D is the crystallite size, λ the wavelength of the X-ray beam, i the full width half maximum corrected of the instrumental width (0.06°) and  the diffraction peak angle.

Transmission electron microscopy (TEM) images were acquired using a JEOL 2010 with a 200 kV accelerating voltage equipped with a LaB6 source and a CCD Gatan camera. The spatial resolution was 0.19 nm. The TEM device was coupled with energy dispersive (EDS) spectrometer (Oxford Instruments).

Scanning electron microscopy (SEM) analyses have been used to follow the textural evolution of the catalyst mixture after activation and after catalytic test. SEM images were recorded on a Zeiss Merlin Compact, using the secondary electron detector and an acceleration voltage of 5 kV. Elemental composition was probed by switching the accelerating voltage to 15 kV and using the EDX (energy dispersive X-Ray) detector.

Textural analyses of MnOx oxides and SAPO-34 zeolites were performed by N 2 physisorption at -196 °C on a micromeritic ASAP 2020 instrument after desorption of the solid at 400 °C for 2 h under vacuum of 0.1 Pa. The BET method was used to determine the BET surface area, the BJH method to determine the pore volume and size and the t-plot method to determine microporosity. NH 3 -TPD measurements were performed to determine the quantity and strength of acid sites of SAPO-34 using a BelCat-M (Bel Japan Inc.) apparatus equipped with a TCD detector. First around 60 mg of solid was flushed under He flow at 400 °C for 1 h to remove adsorbed molecules and the solid was exposed to 50 nml.min -1 of 1% NH 3 -He for 30 min. After flushing the sample under He at 100 °C until a stable TCD signal was observed, the temperature was raised at a rate of 10 °C/min under 50 nmL.min - 1 of He. The number of acid sites was determined by integration over time of the quantity of NH 3 desorbed.

Catalytic testing has been performed with a home-made apparatus containing the reaction and pretreatment gas inlet lines of CO, H 2 and N 2 . Each line was composed of a Brooks 5850 TR flow regulator, a manometer and a stop valve. A straight tubular steel reactor (internal diameter of 6 mm) containing powder samples and heated with a tubular furnace was used. A thermowell with an external diameter of 1.6 mm passed through the whole reactor allowing temperature measurement across the whole catalytic bed which was centered in the reactor using quartz wool. The gas at the outlet of the reactor was flowing either to a vent or to a manual pressure regulator. All the lines after the reactor were heated at 100 °C to avoid condensation of liquid products. The gas analysis has been performed at atmospheric pressure with two microGC chromatographs equipped with a TCD detector: the first one contained a molecular sieve and an alumina Na2SO4 column for separation of CO, H 2 , CH 4 and N 2 and of C2 to C6 hydrocarbons, respectively. The second one (3000/Inficon) was equipped with a Plot-Ucolumn for analysis of CO 2 . The carbon balance was 100 ±5 %.

The catalyst was activated at atmospheric pressure 20%H 2 -N 2 at 430 °C for 4 h. Then, the reactor was flushed under N 2 , the pressure increased to 25 bars and the temperature set to 410 °C. The reactants were preloaded in their respective inlet lines to 25 bars before setting the flow rates and opening the inlet valves. Usually, the catalytic measurements lasted for at least 14 h to reach an apparent steady state.

Figure 1 .

 1 Figure 1. XRD diffractograms of (a) MnO x and (b) 90%MnO x +10%SAPO34-SM after thermal treatment under H 2 flow, (c) SAPO-34 after drying and (d) calcination. The diffraction peaks of (◆) SAPO-34, (•) MnO, (◼) Mn 3 O 4 , () Al(OH) 3 Bayerite and () Al(OH) 3 Nordstrandite were indexed with the PDF 47-0429, 75-1090, 01-1127, 74-119 and 72-0623 ICDD patterns, respectively.

Figure 2 .

 2 Figure 2. TEM images of a) the prepared MnCO 3 nanoparticles before thermal treatment. b) zoom on a single nanoparticle showing that it is porous and constituted of secondary nanoparticles c) SEM image of the prepared MnO x nanoparticles after thermal treatment, TEM images of SAPO-34 sample showing d,e) zeolite crystals and f) extra-framework alumina.

Figure 3 :

 3 Figure 3: SEM images of a) 90%MnO x +10%SAPO-34-SM sample after thermal treatment under H 2 flow, of b) fresh 90%MnO x +10%SAPO-34-MM sample showing aggregates of MnO x on the left and one of SAPO-34 in the down right corner and c) zoom of this image showing MnO x and SAPO-34 separation. SAPO-34 crystals are colored in red and MnOx nanoparticles in green.

Figure 4 .

 4 Figure 4. (a) evolution of the X(CO) conversion and O/P ratio with time on stream, (b) evolution of the hydrocarbon selectivity with time on stream for MnO x +SAPO-34-MM (full circles) and MnO x +SAPO-34-SM (empty triangles). Reaction conditions: m(MnO x )=270 mg, m(SAPO-34)=30 mg, P=25 bar, T=410 °C, H 2 /CO/N 2 =60/30/10, WHSV 87 mL.min -1 .g -1 .

Figure 5 .

 5 Figure 5. SEM images of 90%MnO x +10%SAPO-34-MM after activation under 20 nml/min of H 2 at 430 °C for 4 h and (a) 0 h, (b) 2 h, (c) 14 h on stream and (d) SEM images of 90%MnO x +10%SAPO-34-SM after activation under 20 nml/min of H 2 at 430 °C for 4 h and 14 h on stream. The needles in Figure 5b correspond to quart wool added to the catalytic bed. Reaction conditions: m(MnO x ) =270 mg, m(SAPO-34) =30 mg, P=25 bar, T=410 °C, H 2 /CO/N 2 =60/30/10, WHSV 87 mL.min -1 .g -1 .

Figure 6 .

 6 Figure 6. SEM images of 90%MnO x +10%SAPO-34-MM (a) after activation under 20 nml.min-1 of H 2 at 430 °C for 4 h, b) after activation and 2 h on stream, (c) after activation and 14 h on stream and d) 90%MnO x +10%SAPO-34-SM after activation under 20 nml.min -1 of H 2 at 430 °C for 4 h and 14 h on stream. SAPO-34 crystals are colored in red and MnO x nanoparticles in green. Reaction conditions: m(MnO x )=270 mg, m(SAPO-34)=30 mg, P=25 bar, T=410 °C, H 2 /CO/N 2 =60/30/10, WHSV 87 mL.min -1 .g - 1 .

Table 1 .

 1 X(CO) conversion and O/P ratio, CO 2 , CH 4 and hydrocarbon selectivity for MnO x +SAPO-34-MM and MnO x +SAPO-34-SM. Reaction conditions: m(MnO x ) =270 mg, m(SAPO-34) =30 mg, P=25 bar, T=410 °C, H 2 /CO/N 2 =60/30/10, WHSV 87 mL.min -1 .g -1 . Data were taken after 14 h on stream.

		Selectivity (%)
	X(CO) (%)	O/P	S(CH4) S(CO2) S(C2) S(C3) S(C4) S(C5+)
	MnOx-+SAPO-34-MM 4.1	16.2 12	33	24	25	4	2
	MnOx
	+SAPO-34-SM 4.4	16.8 9	33	25	27	4	2

Acknowledgements

The French National Research agency 'Agence Nationale de la Recherche' (ANR) is acknowledged for the financial support of the INCH project (ANR-17-CE07-0011).

Keywords: syngas • light olefins • OXZEO process• intimacy • in situ textural evolution

Entry for the Table of Contents

The intimacy of a mechanical mixture composed of MnO x and SAPO-34 agglomerates improves during the conversion of syngas into light olefins. Such in situ generation of intimacy led to high dispersion of SAPO-34 crystals among MnO x primary particles as obtained for fresh MnO x and SAPO-34 suspension mixture and could explain convergence of the catalytic properties after 14 h on stream.