Computational intelligence-based ocean characteristics forecasting applications, such as Significant Wave Height (SWH) prediction, are crucial for avoiding social and economic loss in coastal cities. Compared to the traditional empirical-based or numerical-based forecasting models, "soft computing" approaches, including machine learning and deep learning models, have shown numerous success in recent years. In this paper, we focus on enabling the deep learning model to learn both short-term and long-term spatial-temporal dependencies for SWH prediction. A Wavelet Graph Neural Network (WGNN) approach is proposed to integrate the advantages of wavelet transform and graph neural network. Several parallel graph neural networks are separately trained on wavelet decomposed data, and the reconstruction of each model's prediction forms the final SWH prediction. Experimental results show that the proposed WGNN approach outperforms other models, including the numerical models, the machine learning models, and several deep learning models.

There is a natural synergy (yes, we're using that word) among the many subcategories that make up the AI world. It would be impossible to talk about synthetic data without talking about machine learning, computer vision, software, ethics, privacy (and neural rendering, and GANs, and our Marketing and Sales director Michael's daughter's book Neural Networks for Babies) -- so that's why we don't do that. But synthetic data remains the apple of our eye. So we were thrilled to discover that Gartner Inc.'s June Report predicts that by 2030, the most used type of data in AI will be synthetic. Modernization can be a tricky thing, especially when it requires industry-wide adjustments.

Tropical cyclones can be of varied intensity and cause a huge loss of lives and property if the intensity is high enough. Therefore, the prediction of the intensity of tropical cyclones advance in time is of utmost importance. We propose a novel stacked bidirectional long short-term memory network (BiLSTM) based model architecture to predict the intensity of a tropical cyclone in terms of Maximum surface sustained wind speed (MSWS). The proposed model can predict MSWS well advance in time (up to 72 h) with very high accuracy. We have applied the model on tropical cyclones in the North Indian Ocean from 1982 to 2018 and checked its performance on two recent tropical cyclones, namely, Fani and Vayu. The model predicts MSWS (in knots) for the next 3, 12, 24, 36, 48, 60, and 72 hours with a mean absolute error of 1.52, 3.66, 5.88, 7.42, 8.96, 10.15, and 11.92, respectively.

Anyone working in data management and data science can attest to the challenge and time-consuming nature of mapping a set of data from a new source into a platform where it can be cleaned, validated, and ultimately analyzed and used to train algorithms. After all, your algorithms are only as good as the data used to train them. Now imagine if these data sets are coming from hundreds of external users who have employed any number of systems to collect this data, from Excel files to actual shoeboxes full of photos. That is the challenge that non-profit wildlife conservation machine learning and artificial intelligence service provider Wild Me has faced over its more than a decade of operation. The organization builds open software and AI for the conservation research community.

Machine learning is gaining popularity across scientific and technical fields, but it's often not clear to researchers, especially young scientists, how they can apply these methods in their work. In many ways, ESS present ideal use cases for ML applications because the problems being addressed--like climate change, weather forecasting, and natural hazards assessment--are globally important; the data are often freely available, voluminous, and of high quality; and computational resources required to develop ML models are steadily becoming more affordable. Free computational languages and ML code libraries are also now available (e.g., scikit-learn, PyTorch, and TensorFlow), contributing to making entry barriers lower than ever. Nevertheless, our experience has been that many young scientists and students interested in applying ML techniques to ESS data do not have a clear sense of how to do so. An ML algorithm can be thought of broadly as a mathematical function containing many free parameters (thousands or even millions) that takes inputs (features) and maps those features into one or more outputs (targets).

We propose a curriculum-driven learning strategy for solving difficult multi-agent coordination tasks. Our method is inspired by a study of animal communication, which shows that two straightforward design features (mutual reward and decentralization) support a vast spectrum of communication protocols in nature. We highlight the importance of similarly interpreting emergent communication as a spectrum. We introduce a toroidal, continuous-space pursuit-evasion environment and show that naive decentralized learning does not perform well. We then propose a novel curriculum-driven strategy for multi-agent learning. Experiments with pursuit-evasion show that our approach enables decentralized pursuers to learn to coordinate and capture a superior evader, significantly outperforming sophisticated analytical policies. We argue through additional quantitative analysis -- including influence-based measures such as Instantaneous Coordination -- that emergent implicit communication plays a large role in enabling superior levels of coordination.

Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society. One method that has previously been employed is the Dynamic Mode Decomposition (DMD) algorithm - also known as the Linear Inverse Model - which fits linear dynamical models to data. While the DMD usually approximates non-linear terms in the true dynamics as a linear system with random noise, we investigate an extension to the DMD that explicitly represents the non-linear terms as a neural network. Our weight initialization allows the network to produce sensible results before training and then improve the prediction after training as data becomes available. In this short paper, we evaluate the proposed architecture for simulating global sea surface temperatures and compare the results with the standard DMD and seasonal forecasts produced by the state-of-the-art dynamical model, CFSv2.

Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which largely ignore the graph structure during encoding and lack elaborate pre-training tasks to explicitly model graph-text alignments. To tackle these problems, we propose a graph-text joint representation learning model called JointGT. During encoding, we devise a structure-aware semantic aggregation module which is plugged into each Transformer layer to preserve the graph structure. Furthermore, we propose three new pre-training tasks to explicitly enhance the graph-text alignment including respective text / graph reconstruction, and graph-text alignment in the embedding space via Optimal Transport. Experiments show that JointGT obtains new state-of-the-art performance on various KG-to-text datasets.

Among the many adverse impacts of climate change in the most vulnerable countries, climate change–induced displacement increasingly caused by extreme weather events is a serious concern, particularly in densely populated Asian countries. Reports by the Intergovernmental Panel on Climate Change (IPCC) project a grim picture for South Asia, the most populous region on Earth, home to about one-quarter of global population, with the highest poverty incidence. A combination of poor socioeconomic indicators and increased frequency and intensity of cyclones and floods renders the region extremely vulnerable. Meanwhile, slow-onset climate hazards, such as sea level rise, salinity intrusion, water stress, and crop failures gradually turn into larger disasters. Within South Asia, Bangladesh stands as the most vulnerable: 4.1 million people were displaced as a result of climate disasters in 2019 (2.5% of the population), 13.3 million people could be displaced by climate change by 2050, and 18% of its coastland will remain inundated by 2080 ([ 1 ][1]). We describe how, faced with such natural and human-made adversities, Bangladesh can stand as a model of disaster management, adaptation, and resilience. The Paris Agreement goal of keeping the temperature rise at 1.5°C or well below 2°C compared to pre-industrial times may not be achieved, given the lack of ambitious mitigation. As a result, the number of people estimated to be displaced by slow-onset events will stand at ∼22.5 million by 2030 and ∼34.4 million by 2050 ([ 2 ][2]). A combination of sudden and slow-onset climate events, which affect all elements of the environment, becomes the main driver of environmental displacement. Migration is an adaptation strategy. An estimated half a million people move to Dhaka, the capital city of Bangladesh, each year. Migration of this magnitude presents a challenge for Bangladesh given its small land area (147,570 km2) and high population density (∼1100/km2). There is simply little space for retreat: Bangladesh's population is half that of the United States, living on ∼1.5% of the land area of the United States. Usually, three pathways can be discerned with respect to how displaced people are settled: autonomous relocation by displaced individuals (without much government support), government-supported temporary settlement, and planned relocation. In Bangladesh, the first option overwhelms, followed by efforts for temporary settlement, until the government rehabilitates their former residences. Planned relocation or managed retreat in response to climate change ([ 3 ][3]) is not yet happening widely because of space and resource constraints. ![Figure][4] Building migrant-friendly, climate-resilient cities in Bangladesh The map shows some activities being undertaken to build migrant-friendly and climate-resilient cities in Bangladesh. Descriptions of activities are based on publicly available information about the programs, and on discussions with representatives of the NGO BRAC. GRAPHIC: N. DESAI/ SCIENCE Since the founding of Bangladesh in 1971, and even earlier in Pakistan, government-planned relocation of people displaced by riverbank erosion has fueled ethnic conflicts in the Chittagong Hill Tracts in the southeast part of the country, because the move was not backed by consultations with tribal communities. About 100,000 of more than a million Rohingya refugees in Bangladesh, fleeing persecution in Myanmar, are being relocated to Bhasan Char, an island in the Bay of Bengal. In land-hungry Bangladesh, most of the 30+ such Chars/mudflats in the bay are already inhabited at different degrees by people displaced by riverbank erosion and climate change. Despite these odds, Bangladesh is a leader in economic growth among developing countries and in mainstreaming climate change into its development strategy. Partially in response to scientific findings, the National Strategy on the Management of Disaster and Climate Induced Internal Displacement (N SMDCIID) adopted in 2015 incorporated disaster risk reduction and rights-based approaches, so that vulnerable communities can enjoy their basic rights to livelihood, food, health, and housing. The Strategy is built on an integrated Displacement Management Framework, in line with the migration management cycle of the International Organization of Migration (IOM). This Framework elaborates responses during the three phases of mobility management: pre-displacement [disaster risk reduction (DRR)], displacement (emergency), and post-displacement (rehabilitation/relocation). Under the Strategy, the government has initiated support for livelihood opportunities, housing, and human development of displaced people in vulnerable hotspots. It is likely that the government-supported community mobilization and disaster management and DRR policies, both before and after adoption of this Strategy, were helpful in lessening the number of casualties from the supercyclone Amphan in May 2020. One way to address displacements under increasing urbanization across the world could be the establishment of peri-urban growth centers and transformation of cities and towns to be migrant-friendly. This option appears practicable for populous countries such as Bangladesh, having little space for retreat from vulnerable hotspots. To achieve this, institutional changes in a city need to be fostered by research, planning, design, and capacity building. Examples from cities such as Durban, Quito, Semarang, and Malé indicate that cities may need to develop general as well as sector-based strategies to manage effective climate change adaptation ([ 4 ][5]). This warrants the linking of adaptation planning and implementation to city priorities. Cities must have access to reliable information and opportunities to share experiences through local, regional, national, and international networks ([ 4 ][5]). National and local governments should develop migrant-friendly plans along three lines: building of resilient hardware, such as low-cost housing, industries for employment generation, and other infrastructure; software, such as legal, policy, and institutional frameworks; and “heart-ware”—the promotion of awareness, reflecting values and ethics. The basic parameters for safe and orderly movement for migrants are to ensure employment, social protection, access to education, housing, health services, utilities, etc. Although government support is important, engagement of the private sector, nongovernmental organizations (NGOs), civil society, and university-led research can strengthen municipal adaptation efforts. This is what the International Centre for Climate Change and Development (ICCCAD) in Bangladesh has been doing—to facilitate the transformation of smaller peripheral towns to be migrant-friendly as a climate adaptation strategy (see the figure). Our work has multiple purposes: to shift the tide of migration away from Dhaka and other large cities toward smaller towns, and to decentralize climate-resilient development and facilitate planning for basic services and amenities. In Bangladesh, a majority of those displaced by climate change prefer non-migration from their ancestral roots ([ 5 ][6]) if they are provided support for improving their livelihood, housing, etc. Settlement of displaced people in a town nearer to their ancestral home allows them to maintain psychological kinship and cultural comforts. On the basis of such local context and needs, each migrant-friendly town needs its own development and adaptation plans to address climate risks and economic opportunities. The NGO BRAC has initially identified about 20 towns and municipalities, considering their economic potential and climate stress, to determine whether they can absorb a sizeable number of displaced people. A number of satellite towns adjacent to economic hubs, such as relatively elevated sea and river ports and export processing zones (EPZs), can potentially employ millions of migrants. Investment in manufacturing and/or services is generating jobs through public, private, and community partnerships, such as private investments, government support, and microfinancing from BRAC and Grameen Bank. ICCCAD has formal agreements with many ministries and agencies including the Local Government Engineering Department (LGED), the agency for building and maintenance of rural infrastructure. ICCCAD has been working as an advisor and co-implementer of programs with all stakeholders, including mayors in two small towns in coastal Bangladesh, Mongla and Noapara (see the figure). It is helping town authorities in planning and implementing initiatives that are intended to be hospitable to incoming settlers, so that they can gradually be mainstreamed into citizenship ([ 6 ][7]). The process is based on a participatory, consultative process involving the municipal authorities, host community leaders, and settlers. The Strategy (NSMDCIID) includes options such as supporting livelihood for new settlers and skill development, both in displacement hotspots and in new settlements. Although these towns do not yet have adaptation plans as such, the programs consider risk-informed and socially conducive adaptation measures. BRAC with its Climate Bridge Fund is also currently implementing different programs in five cities: Khulna, Rajshahi, Satkhira, Barisal, and Sirajgonj. For programs under implementation in these cities, the target groups are incoming migrants, who crowd the slums. The activities undertaken in these cities are similar, with some specific activities in each town (see the figure). Most of the new settlers have moved from rural areas rendered inhospitable as a consequence of slow and sudden-onset climate impacts. ICCCAD started facilitating this program 3 years ago with a strategy of learning by doing. Among the lessons learned: (i) Vibrant economic activities in these rapidly growing towns are absorbing increasing numbers of migrants from vulnerable hotspots, and (ii) migrants with energy and agency are engaging themselves in different small businesses, with government support and microcredits from Grameen Bank and BRAC. The fact that an overwhelming share of those displaced by climate change around the world resettle internally indicates that adaptation in-country is the most viable option. The global community dealing with disaster displacement, including the United Nations Framework Convention on Climate Change (UNFCCC), primarily recommends this option. However, it requires adequate international support, which developed countries are obligated to deliver (with the language “shall provide”) under the UNFCCC and the Paris Agreement. Unfortunately, adaptation finance continues to remain the “poor cousin” of mitigation, the ratio remaining 20:80 despite repeated pledges by developed countries and agencies. For domestic resource mobilization, some countries (for example, Fiji) have introduced an adaptation levy on all goods and services produced and consumed in the country. There are limits to relocation in-country; sudden and slow-onset events sometimes trigger cross-border movement of individuals seeking jobs and protection. The UN Commission on Human Rights argues for looking at such mobility from a human-rights perspective (i.e., the space for realizing the basic human rights of livelihood, health, housing, etc.). Currently, those displaced by climate change suffer an international protection deficit, not qualifying as “refugees” under the 1951 Geneva Convention. Consideration of those displaced by climate change began in 2008 under the UNFCCC, with research and advocacy. The Cancun Adaptation Framework (Decision 1./CP16, paragraph 14f ) provides for different types of climate-induced human mobility (displacement, migration, and planned relocation), different scales of mobility (national, regional, and international), and different actions (research, cooperation, and coordination). This decision recognized migration as an adaptation strategy. The Nansen Initiative in 2011–2012 focused on promoting research and planned relocation. The Paris Agreement established a Task Force on Displacement under the Warsaw International Mechanism, with mandates to make recommendations for averting, minimizing, and addressing climate change–induced displacement. Finally, the Global Compact on Safe, Orderly, Regular and Responsive Migration was adopted in 2018 as the first multilateral framework to cooperate on migration, including in response to climate change. Many major countries and think tanks started looking at climate displacement through a lens of national security, with its characterization as a “threat multiplier,” and a number of nationally determined contributions under the Paris Agreement refer to those displaced by climate change as potentially fueling national and regional conflicts ([ 7 ][8]). However, climate security can be looked at either from a conflict perspective or from a lens of vulnerability-focused human and global security ([ 8 ][9]). The “conflict view” proponents call for closing the borders, but still the result of such a policy ends up being a humanitarian disaster, caused primarily by actions beyond the control of those being displaced or of their home countries. Should we see more of these displaced and disgruntled youth as victims in the hands of human traffickers? If not, we then argue—viewing this displacement in terms of vulnerability-focused human security—that planned relocation internationally can be an effective way forward under paragraph 14f of the Cancun agreement. As multilateral processes are typically very cumbersome and painstakingly slow, bilateral action can be more rapid and effective, and may then gradually feed into regional and global initiatives. For example, the Seasonal Migrant Worker Program in Australia and New Zealand, or New Zealand's Climate Visa Program ([ 9 ][10]), attract migrants from the Pacific Small Island States (although these initiatives are not solely meant for absorbing migrants displaced by climate change). Canada and the United States offered immigration opportunities to typhoon Haiyan victims, but these were based on kinship relations ([ 10 ][11]). Although the EU does not have a common policy, Finland and Sweden changed their earlier liberal policies on climate-induced displacement after the refugee influx from Syria ([ 11 ][12]). There are also provisions of circular migration, as between Spain and Colombia. The IOM continues recommending such migration between developed and developing countries as an adaptation response to climate-induced vulnerability. The Bangladesh Strategy recommends such options as well. Many developed countries already suffer from demographic deficits, with negative growth, and increasingly aging cohorts. The rhetoric in many of these counties, which often is anti-immigrant, cannot change the reality that these countries will need more and more young and skilled labor. Using projected needs of specific skills, developed countries could thus enter into bilateral agreements with climate-vulnerable countries, where those displaced by climate change may be trained in jointly supported educational and training institutions, either for permanent or for circular migration. For example, under the “Triple Win” program, Germany recruits nurses from Serbia, Bosnia-Herzegovina, and the Philippines to meet their nursing shortage, while reducing unemployment and contributing to economic development in the countries of origin ([ 12 ][13]). It is only just and fair for developed-country emitters of greenhouse gases to take some responsibility under Article 3.1 of the UNFCCC for their disproportionate contributions to generating this increasing number of people displaced by climate change. Lessons suggest that migration to rich countries can have strong positive impacts on labor market, GDP growth, and public revenue for host countries ([ 13 ][14], [ 14 ][15]). Mig ration is also typically positive for countries of origin, through remittance, transfer of technology, skills, domestic consumption and GDP growth, housing, children's education, and more. In 2017, low- and middle-income countries received more than $466 billion in remittances, three times the amount of official aid ([ 15 ][16]). This presents an important indicator of the effects that bilateral agreements on migration of climate-displaced people may have on promoting many different Sustainable Development Goals. Such migration should be framed as a win-win option, not as climate humanitarianism ([ 10 ][11]). The Bangladesh Strategy (NSMDCIID) argues for creating “opportunities for international labor migration by one or few members of families from the displacement hotspots” (p. 115). Older and underage family members and spouses can stay behind and rebuild their lives with remittance support. We believe this option of selective, not wholesale, relocation as a pragmatic policy can be scaled gradually, as warranted by projected demands of skills over time in developed countries. This relocation is based on bilateral planning and preparation, unlike the conventional, voluntary migration of skilled labor to industrial countries. This option is challenging, though mutually rewarding. However, acceptance of this proposal by Western democracies depends on whether they are ready to embrace and enjoy more of “smart/pooled” sovereignty, with enlightened self-interests under climate-induced vulnerability interdependence, rather than holding on to a centuries-old “Westphalian” model of a zero-sum game in global cooperation. Many have argued that with the increasing number of global commons problems, we now live in a positive-sum world. But such a paradigm shift warrants a vigorous campaign to raise awareness among citizens in industrial countries about the “new normal” of increasing extreme and ever-growing slow-onset events. Those citizens and politicians must face the lead and obligatory responsibility their countries have assumed under the international climate regime to support adaptation in vulnerable countries. Such awareness must confront and overcome the xenophobia and anti-immigration sentiments that often surface in many countries, inhibiting the enjoyment of mutual dividends, which can contribute to real and sustainable global peace and security. Successful implementation of the two options raised above (migrant-friendly towns and bilateral agreements for international migration) could help to germinate coordinated implementation, as stipulated in the Cancun agreement, of global policy frameworks on climate change (UNFCCC), disaster risk reduction (Sendai Framework), and human migration (Global Compact for Migration). As many ideas and actions on planned internal or international relocation of climate change–induced displacement are relatively new in the national and global policy domains, continued research and science-policy interface are essential in order to determine the feasibility, efficacy, and scalability of these options. 1. [↵][17]1. K. Rigaud et al ., “Groundswell: Preparing for Internal Climate Migration” (World Bank, 2018). 2. [↵][18]1. H. Singh, 2. J. Faleiro, 3. T. Anderson, 4. S. Vashist , “Costs of Climate Inaction Displacement and Distress Migration” (Actionaid, 2020). 3. [↵][19]1. J. Carmin, 2. D. Roberts, 3. I. Anguelovski , “Planning Climate Resilient Cities: Early Lessons from Early Adapters” (2011), pp. 5–8. 4. [↵][20]1. S. Weerasinghe et al ., “Planned Relocation, Disasters and Climate Change: Consolidating Good Practices and Preparing for the Future” (UNHCR, 2014). 5. [↵][21]1. B. Mallick, 2. K. G. Rogers, 3. Z. Sultana , Ambio 10.1007/s13280-021-01552-8 (2021). 6. [↵][22]1. S. S. Alam, 2. S. Huq, 3. F. Islam, 4. H. M. A. Hoque , “Building Climate-Resilient, Migrant-Friendly Cities and Towns” (International Centre for Climate Change and Development, 2018). 7. [↵][23]1. E. Wright, 2. D. Tänzler, 3. L. Rüttinger , “Migration, Environment and Climate Change: Responding via Climate Change Adaptation Policy” (German Environment Agency, 2020). 8. [↵][24]1. M. R. Khan , Toward a Binding Climate Change Adaptation Regime: A Proposed Framework (Routledge, 2014), chapter 6. 9. [↵][25]1. H. Dempster , “New Zealand's ‘Climate Refugee’ Visas: Lessons for the Rest of the World” (Centre for Global Development, Washington, DC, 2020). 10. [↵][26]1. D. M. S. Matias , Clim. Change 160, 143 (2020). [OpenUrl][27] 11. [↵][28]1. A. Kraler, 2. K. Caitlin, 3. M. Wagner , “Climate Change and Migration: Legal and Policy Challenges and Responses to Environmentally-Induced Migration” (European Union, 2020). 12. [↵][29]German Development Agency, “Sustainable Recruitment of Nurses (Triple Win)” (2019); [www.giz.de/en/worldwide/41533.html][30]. 13. [↵][31]1. E.-j. Quak , “The effects of economic integration of migrants on the economy of host countries” (Institute of Development Studies, London, 2016). 14. [↵][32]1. V. Grossmann , “How Immigration Affects Investment and Productivity in Host and Home Countries” (IZA, 2016); . 15. [↵][33]World Bank, “Record high remittances to low- and middle-income countries in 2017” (2018); [www.worldbank.org/en/news/press-release/2018/04/23/record-high-remittances-to-low-and-middle-income-countries-in-2017][34]. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: pending:yes [5]: #ref-4 [6]: #ref-5 [7]: #ref-6 [8]: #ref-7 [9]: #ref-8 [10]: #ref-9 [11]: #ref-10 [12]: #ref-11 [13]: #ref-12 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: #xref-ref-2-1 "View reference 2 in text" [19]: #xref-ref-3-1 "View reference 3 in text" [20]: #xref-ref-4-1 "View reference 4 in text" [21]: #xref-ref-5-1 "View reference 5 in text" [22]: #xref-ref-6-1 "View reference 6 in text" [23]: #xref-ref-7-1 "View reference 7 in text" [24]: #xref-ref-8-1 "View reference 8 in text" [25]: #xref-ref-9-1 "View reference 9 in text" [26]: #xref-ref-10-1 "View reference 10 in text" [27]: {openurl}?query=rft.jtitle%253DClim.%2BChange%26rft.volume%253D160%26rft.spage%253D143%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: #xref-ref-11-1 "View reference 11 in text" [29]: #xref-ref-12-1 "View reference 12 in text" [30]: http://www.giz.de/en/worldwide/41533.html [31]: #xref-ref-13-1 "View reference 13 in text" [32]: #xref-ref-14-1 "View reference 14 in text" [33]: #xref-ref-15-1 "View reference 15 in text" [34]: http://www.worldbank.org/en/news/press-release/2018/04/23/record-high-remittances-to-low-and-middle-income-countries-in-2017

Habitability loss is increasingly recognized as an important dimension of climate risk assessment and one with complex linkages to migration. Most habitability assessments, like climate risk assessments more generally, are based on “top-down” approaches that apply quantitative models using uniform methodologies and generalizable assumptions at global and regional scales, privileging physical sciences over social science–informed understandings of local vulnerability and adaptive capacity. Many assessments have focused on a single climate hazard threshold (such as permanent inundation or the 1-in-100-year flood), and a subset have implied that outmigration may be one of the few viable adaptation responses ([ 1 ][1]). There is a risk that such climate determinism minimizes the potential for human agency to find creative, locally appropriate solutions. Although top-down modeling can serve a useful purpose in identifying potential future “hot spots” for habitability decline and potential outmigration, only by integrating “bottom-up” insights related to place-based physical systems and social contexts, including potential adaptive responses, will we arrive at a more nuanced understanding. This integrated framework would encourage development of policies that identify the most feasible and actionable local adaptation options across diverse geographies and groups, rather than options that are deterministic and one-size-fits-all and encourage binary “migrate or not” decisions. We propose a set of recommendations centered around building the research and assessment knowledge base most needed to inform policy responses around habitability loss and migration. We define habitability as the environmental conditions in a particular setting that support healthy human life, productive livelihoods, and sustainable intergenerational development. Climate change may undermine one or more of the following associated, interacting, dimensions of habitability: basic human survival ([ 2 ][2]), livelihood security ([ 3 ][3]), and societies' capacity to manage environmental risks ([ 4 ][4]). Rapid rates of climate change and departures from historical variability ranges can increase risks, especially when coupled with nonclimate stressors. In such instances, threats to habitability may be evident in changing flows of human migration, whether forced or voluntary ([ 5 ][5]). Most habitability assessments have relied on outputs from top-down models. This approach is conducive to system-level prediction, producing quantitative outputs that are globally comparable, such as single physical hazard thresholds that are either assumed or empirically based. Much recent work reflects a blend of long-term, high-resolution historical climate data where available, combined with projections across a large suite of global climate models driven by multiple representative concentration pathways (RCPs) representing trajectories of greenhouse gas concentrations. Another critical element is inclusion of extreme events, often expressed as a frequency of occurrence or a magnitude associated with a given recurrence period. In turn, top-down demographic and economic models, which form the basis for the shared socioeconomic pathways (SSPs) projecting global socioeconomic trajectories, provide a picture of future population and development that can also inform projections of people and assets at risk. Climate projections can also drive sectoral impact assessments—for example, empirically by extending historical statistical relationships between climate variability and the affected sector. More commonly, projections from standardized climate simulations drive sectoral impact models that dynamically simulate key features, such as crop growth. Top-down migration models use relative changes in sectoral impacts across regions along with other information as a means of projecting future population flows. Thus, these models project responses to habitability changes in regions where varying conditions may lead to outmigration, inmigration, or both. The standardized nature of top-down methods facilitates comparisons—for example, of regions most at risk of crossing habitability thresholds associated with a climate hazard, and when. The top-down perspective can also reveal large-scale trends and interconnected features of global systems. However, there are several limitations. First, local and regional geophysical and sector-specific factors can drive hazards and risks at scales missed by global analyses. Second, less-modeled, place-specific characteristics of populations, such as health and socioeconomic status, shape both exposure and vulnerability. Third, adaptation choices and activities are embedded in historical context and culturally specific individual and community values and objectives that cannot easily be incorporated in models. Fourth, high-impact outcomes—associated, for example, with compound extreme events and abrupt changes in climate, ecological, and social systems—may be underestimated because of top-down model limitations such as the inability to credibly resolve evolving correlation structures across variables, space, and time, and key system sensitivities and feedbacks within and across systems ([ 6 ][6]). For example, climate phenomena teleconnected across great distances may lead to “breadbasket” failures in key food-producing regions and price shocks that can seriously reduce food security among vulnerable populations far away from the regions experiencing the climate stress. Fortunately, top-down approaches are increasingly being paired with bottom-up approaches that offer a specificity that can help address these challenges. Bottom-up conceptual and/or computational modeling of complex adaptive systems can be designed to simulate the local experience of losing habitability over time. In the breadbasket case above, models of local responses can be paired with global models of international food trade that set boundary conditions. For example, agent-based models (ABMs) set up simulations with agents empirically calibrated to behaviorally respond to changing environmental conditions: the loss of assets and livelihood opportunities, threats to life, and changing structure of social networks. Modeling can be trained on local data to understand and predict important feedbacks at higher spatial and temporal resolution than is possible with global models. ABMs can be calibrated to examine a range of individual-actor preferences and test the effect of local decision-making to plausibly depict tradeoffs among adaptation options, including migration ([ 7 ][7]). As another bottom-up example, qualitative information can be coproduced with diverse stakeholders, including subject matter experts, to explore high-impact scenarios and local solutions that will be missed by top-down approaches. Of course, bottom-up approaches have their limitations as well. For example, their specificity makes it difficult to compare across geographies and groups, and individual methodological decisions can appear arbitrary. Furthermore, bottom-up computational models such as ABMs are still limited by a lack of empirical data with which to calibrate model parameters. Here, we walk through the habitability challenges of two climate hazard examples, demonstrating the strengths and limitations of top-down approaches and how bottom-up perspectives lead to different policy-relevant insights. ### Sea level rise and extreme sea level events Recent years have seen growing complexity and nuance in assessments. Global assessments have supplemented climate model outputs by considering a broad range of sea level change components and including, for example, expert elicitation as a means of estimating low-probability, high-consequence outcomes ([ 8 ][8]). High-spatial-resolution digital elevation models and consideration of changes in the frequency and intensity of societally relevant metrics such as recurrence intervals and extreme values of coastal high water have been integrated into global products. Using many of the above advances, Kulp and Strauss estimated that the number of people exposed annually to coastal flooding under constant population could increase from 250 million people today to, by 2100, 310 million to 420 million under an intermediate scenario to 380 million to 630 million under a high-end scenario ([ 1 ][1]). Other studies have included changes in storms, hyper-local positive correlations between population density and subsidence, population projections consistent with SSP-RCP combinations, and assets at risk. Additional refinements have focused on specific coastal locations, adding critical context at the expense of global information. For example, Storlazzi et al. framed their assessment of tipping-point risks to atolls around two metrics—annual overwash events that threaten infrastructure, and salinization of groundwater—that are specifically relevant for atolls given their small size, uniformly low elevation, and relative isolation and found that habitability is threatened in most atoll islands by the middle of the 21st century, far sooner than permanent-inundation–based studies would suggest ([ 9 ][9]). Some local studies have included dynamic interaction between coastal waters and adjacent landforms. Other local and regional studies have considered social dimensions of human vulnerability, as well as in situ adaptation, using empirically calibrated agent-based livelihood decision models that span multiple climate, RCP, and SSP scenarios ([ 7 ][7]). The three dimensions of habitability demonstrate why no single coastal flood metric threshold can be determined in a top-down way. For the direct survivability dimension, key factors include future flood control, feasibility of evacuation, and the stochasticity of individual storms. For livelihood, saline intrusion, for example, could benefit some sectors such as specialized aquaculture, even as it harms most sectors and people. And for the societal resilience dimension, large-scale factors such as levels of inequity, strength of governance and social networks, and quality of infrastructure will be critical. As sea levels rise and coastal flooding becomes more common, social, economic, and political factors in some locations will conspire to induce sudden loss of habitability far sooner than physical hazard–based thresholds such as permanent inundation would suggest, as risk perception and long-term economic viability shift. For example, increases in insurance premiums could negatively affect asset values and tax revenues, leading to deteriorating infrastructure and services. The timing of such threshold-crossing cannot be predicted on the basis of top-down models alone. In some instances, shocks can lead to rapid learning, adjustment, and in situ adaptation, at least temporarily. ![Figure][10] Frequent exceedance by 2100 of historically rare climate thresholds Under the high-emissions scenario RCP8.5, at most coastal locations extreme sea level events historically defined as 1-in-100-year events are projected to range in frequency from once per year to more than 10 times per year due to the effects of sea level rise alone. Only point locations where historical event data are available are shown. Projected number of days per year by 2100 exceeding a 33°C wet bulb globe temperature (WBGT) in a high-emissions scenario are also depicted. Under standard assumptions of wind and solar radiation, a WBGT of 33°C corresponds to a wet bulb temperature of roughly 31.5°C. [Sea level data are from figure 4.12 in ([ 8 ][8]); WBGT data are from fig ure 3 in ([ 12 ][11]).] GRAPHIC: N. DESAI/ SCIENCE BASED ON HORTON ETAL. ### Extreme heat Most assessments of future heat hazards have considered temperature only, although recent efforts are increasingly adopting a compound events framework—for example, considering how co-occurring extremes of high temperature and high humidity can modulate threats to habitability. Humid heat is particularly harmful to human health and the ability to engage in outdoor activities. Sherwood and Huber described a wet bulb temperature of 35°C as a threshold above which humans could not survive beyond approximately 6 hours owing to physiological and thermodynamic limits on the ability to cool through perspiration ([ 2 ][2]). Model-based studies have projected that this threshold could be crossed in the Persian Gulf and South Asia during the second half of the 21st Century under a high-emissions scenario ([ 10 ][12]). However, a finer-scale study found that this threshold has already been briefly crossed multiple times in populous cities. Although an absolute habitability threshold exists for the survivability dimension of extreme humid heat, some people will lose their ability to thermoregulate at much lower wet bulb temperatures. Mortality rates of the elderly, those with chronic health conditions, and those involved in strenuous activity rise dramatically well below the 35°C wet bulb threshold. In terms of the livelihood dimension, at ∼3.5°C of global warming above preindustrial levels, de Lima et al. project that in Sub-Saharan Africa and Southeast Asia increases in humid heat may decrease agricultural labor productivity by 30 to 50%, leading to larger agricultural sector impacts than are associated with direct temperature and CO2 effects on crops ([ 11 ][13]). However, air conditioning and other adaptations will enable—indeed, have enabled—some people to continue to live in places that exceed the 35°C threshold. Such an outcome increases inequity because those with no option but to work outdoors, or no access to affordable air conditioning, would be forced to migrate. And even for those with air conditioning, the third dimension of habitability—society's capacity to manage environmental risks—will be tested in unforeseen ways because it will be critical that air conditioning not fail. Sea level rise and extreme humid heat are far from the only climate hazards that have been assessed in the literature for potential habitability thresholds. For example, changes in surface moisture fluxes as mean precipitation and temperature shift are projected to have large impacts on dryland agriculture, fire regimes in forests, and water availability downstream from snow and glacier reservoirs. These and other hazards and impacts may overlap and interact across scales to affect habitability in complex ways, such as by potentially increasing the risk of conflict. Areas where current-day rare extreme sea level and humid heat events will occur with high frequency by the end of the century under a high emissions scenario of sea level rise and warming are identified in the figure ([ 8 ][8], [ 12 ][11]). The two metrics, corresponding to the current 1-in-100-year extreme sea level event and a wet bulb globe temperature of 33°C, respectively, are emblematic of top-down approaches. They thus represent an important point of entry for engagement with the bottom-up insights described above, as a step toward more nuanced habitability and migration assessments. Migration may result from threats to survival, upended livelihoods, or the breakdown in the collective capacity to adapt ([ 5 ][5]). However, research on climate change and migration makes clear that an even broader set of factors undergird migration decision-making. A decision to move is ultimately a personal or household judgment on factors that include local habitability. Involuntary migration occurs when people lack agency about the key dimensions of mobility, including the timing, destination, or duration of mobility or whether to migrate at all. Where agency is extremely low, involuntary migration may take different forms, including temporary or permanent displacement and distress migration. Distress migration—mass migration or displacement related to rapid deterioration in local circumstances—is a humanitarian concern because of the need for emergency interventions to avoid poor outcomes. Distress migration has been a common phenomenon throughout history but has risen and fallen on the global policy agenda largely as a function of whether or not wealthy industrialized countries are destinations. Also of humanitarian concern is the phenomenon of involuntary immobility, in which people are unable to move without help—the population most likely to require assistance relocating under managed retreat programs. Avoiding distress migration and involuntary immobility in favor of safe and orderly migration, as advanced by the Global Compact on Migration, is now a global policy priority, and the Compact calls on governments to “strengthen joint analysis and sharing of information to better map, understand, predict, and address migration movements” as a result of climate change impacts—all of which are essential aspects of habitability assessment. Many assessments posit some form of forced migration as an inevitable outcome of declining habitability. Yet, environmental stress rarely directly results in migration but works through a complex array of economic, demographic, social, and political proximate determinants that both initiate and sustain or modify flows. In any given population exposed to climate risks, different segments of the population respond to hazards differently and at different points in time, and as such, migration evolves with habitability through time. Whereas some may be able to migrate from deteriorating conditions without assistance, others may become immobile owing to limited options and insufficient resources, suffering progressive impoverishment and vulnerability unless social protection or planned relocation efforts are implemented ([ 5 ][5]). In situ adaptation, facilitated migration, and improving reception of migrants in (largely urban) destination areas are often more appropriate policies in these regions. Managed retreat has been proposed as a strategy for regions with declining habitability, but as a largely technical package of responses that includes buyouts, incentives, and planned relocation, among others, it does not currently translate well to most developing-world circumstances. The relationship between habitability and migration may be counterintuitive, as illustrated by the lack of evidence for migration away from low-lying delta areas despite acute risks ([ 7 ][7]). Migration itself affects habitability for those who are unable or unwilling to leave increasingly vulnerable circumstances, either positively, such as through incoming remittances, or negatively, such as through outmigration of the working-age demographic stratum and subsequent changes in economic dynamism and livelihood options. Flows may begin owing to entrenched poverty and environmental risks and then be sustained as migrant social networks lower barriers for those who initially remained behind. Although migration offers possibilities for advancing human well-being, as multiple dimensions of habitability are compromised, resulting forced migration will negatively affect human well-being. Migrants risk new constraints in urban informal settlements, and displaced persons may become permanently disconnected from their original communities and livelihoods in resettlement communities or refugee camps ([ 13 ][14]). Although top-down assessments oversimplify likely migratory responses to habitability declines, this does not necessarily imply that migration flows are overestimated. Multiple factors are driving migration in developing regions to varying degrees, including poor governance, perceived lack of opportunities, conflict, individual extreme events, and in some cases, climate-catastrophic discourses that add to a sense of hopelessness ([ 14 ][15]). Deeper and more contextualized understandings of migration dynamics aid in policy design, but the threats that result from declining habitability in combination with other drivers are real and may lead to substantial displacement of populations across a range of spatial scales. Top-down, threshold-based habitability assessments can serve a critical role in helping to identify priority regions and groups for integrated bottom-up work while revealing interactions in global systems that cannot be gleaned from the bottom-up work alone. Integration not only leads to better predictions of when and where habitability may diminish but also can be used to inform adaptation responses that themselves help preserve or restore habitability. Bottom-up assessments by definition provide finer, local resolution, and their richness of detail means that they require diverse participation and methods. To date, most locales have not been subject to such integrated habitability assessment. We thus encourage transdisciplinary, long-term coupled top-down and bottom-up habitability assessment [for example, ([ 15 ][16])] to complement and augment efforts such as the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), which has contributed so much to our understanding of potential future climate impacts on sectors such as agriculture, water, ecosystems, and health. Initial model intercomparison could focus on what regions and groups face diminishing habitability under different model configurations. Particularly where models agree on potential habitability hot spots, bottom-up modeling experiments could be conducted and compared on specified challenges to human survival, livelihoods, and capacity to manage risk, although standardization would be needed. The Intergovernmental Panel on Climate Change (IPCC) and national efforts can also help to develop this still inchoate middle space between top-down and bottom-up approaches to habitability and migration. Migration is emerging as a cross-cutting theme throughout the current IPCC assessment, and a special report on habitability and migration would both advance the knowledge base and showcase emerging methodologies. As one example, a climate change detection and attribution dimension would help inform dialogues about loss and damage under the Paris Agreement. Likewise, a discussion on migration across the Reasons for Concern commonly used in IPCC assessments ([ 5 ][5]) would allow us to distinguish how climate-induced migration, distress or otherwise, is distinct from other forms of migration. The complexity of the assessment challenge calls for a holistic, people-centric approach in which models, data aggregation, and ethnographic work are all advanced. Sectors such as engineering, hydrology, and reinsurance, that have historically been overreliant on physical models and hazard thresholds, operate at a scale that is ripe for habitability-relevant innovations at the interface between top down and bottom up. In this middle space, models could be used to examine policy scenarios instead of learning occurring exclusively from costly, time-consuming, real-world policy interventions that may put vulnerable people at risk. Greater communication among modelers will be key, and models must be validated with on-the-ground local research. To support migration and habitability modeling specifically, this would include data on when, where, and why people have moved or considered moving, how they define habitability, and the policy conditions that determine mobility outcomes ([ 14 ][15]). Furthermore, bottom-up research must account for the place-specific characteristics of populations—such as assets, livelihood opportunities, and social networks—that shape both exposure and adaptation. Investments in place-based social science thus help address data gaps, providing ground-truthing that will strengthen simulations of the outcomes of interventions. Investments in early-warning systems could help to anticipate where distress migration may happen, a key step in informing policy. The shortcomings of adaptation planning and policy at current risk levels in wealthy countries hint at the global challenges ahead in a changing climate. In the United States, for example, federal and local risk assessments—let alone policies—are not presently centrally coordinated or comparable. There is woefully insufficient funding available for bottom-up adaptation efforts from the better-financed federal level. Policies toward population mobility—whether planned, internal responses or immigration from other countries—vary from inconsistent over time to incoherent and sometimes inhumane. Coproduction of knowledge across diverse groups will be a precondition for any breakthroughs. In some instances, a starting point may be to bring preexisting top-down habitability and migration assessments to communities, provided that community feedback is collected and integrated iteratively and before key policy decisions are made. In other instances, stakeholder engagement may begin with fewer top-down, nonprobabilistic approaches that can be developed with communities, such as storylines and scenarios. Storylines and scenarios lend themselves to exploration of the uncertainties that most influence habitability locally (for example, the potential for changing correlation structures in models) and which adaptation strategies should be explored for which groups. Deeper stakeholder engagement, coupled with the other recommendations above, thus provides a foundation for colearning, iteration, and developing flexible approaches to the challenge of diminishing habitability. To the extent that top-down, threshold-based approaches are used to define habitability universally, there is a risk of assuming a high likelihood of uniform outmigration or concluding with blanket policy recommendations around managed retreat. Basing assessments on nuanced definitions of habitability and integrating top-down with bottom-up approaches could encourage a broader range of policies tailored to specific locations and groups, including regions that have been put forth as likely receiving areas. A focus on the dimensions of habitability presented here, and bottom-up approaches, will invariably alter top-down projections of migration. Under wetbulb temperatures exceeding 35°C, high levels of outmigration from the Persian Gulf may be avoided if air conditioning is widely available and alternative livelihood options develop for those who would otherwise work outdoors. However, there will be regions where social tipping points and a sense of prevailing pessimism about the future—for example, owing to evolving risk perception or disinvestment by the private or public sectors—could contribute to outmigration far sooner and more suddenly than top-down habitability threshold–based methods would suggest. Global, regional, and national migration policies themselves will also play an important role in facilitating or impeding migration. What is already clear is that climate change will result in shifting population distributions and that this process will overall be harmful to the most vulnerable, including those who may be “trapped” in deteriorating circumstances. For the reasons described here, and as a matter of climate justice, many semi-arid regions, much of the tropics, and some low-lying deltas and islands should be high priorities for integrated transdisciplinary work on habitability risks and major investments in adaptation. But only by taking into account the complexities described here will we avoid climate determinism and instead implement proactive policies on adaptation and migration that in particular will address the needs of the most vulnerable. 1. [↵][17]1. S. A. Kulp, 2. B. H. Strauss , Nat. Commun. 10, 4844 (2019). [OpenUrl][18] 2. [↵][19]1. S. C. Sherwood, 2. M. Huber , Proc. Natl. Acad. Sci. U.S.A. 107, 9552 (2010). [OpenUrl][20][Abstract/FREE Full Text][21] 3. [↵][22]1. T. Tanner et al ., Nat. Clim. Chang. 5, 23 (2015). [OpenUrl][23] 4. [↵][24]1. J. Barnett, 2. W. N. Adger , Annu. Rev. Environ. Resour. 43, 245 (2018). [OpenUrl][25] 5. [↵][26]1. R. McLeman et al ., Clim. Change 165, 24 (2021). [OpenUrl][27] 6. [↵][28]1. N. Simpson et al ., One Earth 4, 489 (2021). [OpenUrl][29] 7. [↵][30]1. A. R. Bell et al ., Environ. Res. Lett. 16, 024045 (2021). [OpenUrl][31] 8. [↵][32]1. H.-O. Pörtner et al. 1. M. Oppenheimer et al ., in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, H.-O. Pörtner et al., Eds. (IPCC, 2019). 9. [↵][33]1. C. D. Storlazzi et al ., Sci. Adv. 4, eaap9741 (2018). [OpenUrl][34][FREE Full Text][35] 10. [↵][36]1. C. Raymond, 2. T. Matthews, 3. R. M. Horton , Sci. Adv. 6, eaaw1838 (2020). [OpenUrl][37][FREE Full Text][38] 11. [↵][39]1. C. Z. de Lima et al ., Environ. Res. Lett. 16, 044020 (2021). [OpenUrl][40] 12. [↵][41]1. D. Li, 2. J. Yuan, 3. R. E. E. Kopp , Environ. Res. Lett. 15, 064003 (2020). [OpenUrl][42] 13. [↵][43]1. A. Heslin et al ., in Loss and Damage from Climate Change (Springer, 2019), pp. 237–258. 14. [↵][44]1. H. Adams, 2. S. Kay , Environ. Sci. Policy 93, 129 (2019). [OpenUrl][45] 15. [↵][46]1. K. Grace, 2. S. Siddiqui, 3. B. F. Zaitchik , Nat. Food 2, 1 (2021). [OpenUrl][47] Acknowledgments: The authors thank four anonymous reviewers and C. Lesk for comments and K. MacManus for assistance with the map figure. R.M.H. and A.d.S. were supported by the Columbia Climate School and its Earth Institute, and A.d.S. received funding from NSF award 1934978. [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-7 [8]: #ref-8 [9]: #ref-9 [10]: pending:yes [11]: #ref-12 [12]: #ref-10 [13]: #ref-11 [14]: #ref-13 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: {openurl}?query=rft.jtitle%253DNat.%2BCommun.%26rft.volume%253D10%26rft.spage%253D4844%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [19]: #xref-ref-2-1 "View reference 2 in text" [20]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%2BU.S.A.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.0913352107%26rft_id%253Dinfo%253Apmid%252F20439769%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [21]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMToiMTA3LzIxLzk1NTIiO3M6NDoiYXRvbSI7czoyMzoiL3NjaS8zNzIvNjU0OC8xMjc5LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [22]: #xref-ref-3-1 "View reference 3 in text" [23]: {openurl}?query=rft.jtitle%253DNat.%2BClim.%2BChang.%26rft.volume%253D5%26rft.spage%253D23%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [24]: #xref-ref-4-1 "View reference 4 in text" [25]: {openurl}?query=rft.jtitle%253DAnnu.%2BRev.%2BEnviron.%2BResour.%26rft.volume%253D43%26rft.spage%253D245%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: #xref-ref-5-1 "View reference 5 in text" [27]: {openurl}?query=rft.jtitle%253DClim.%2BChange%26rft.volume%253D165%26rft.spage%253D24%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: #xref-ref-6-1 "View reference 6 in text" [29]: {openurl}?query=rft.jtitle%253DOne%2BEarth%26rft.volume%253D4%26rft.spage%253D489%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: #xref-ref-7-1 "View reference 7 in text" [31]: {openurl}?query=rft.jtitle%253DEnviron.%2BRes.%2BLett.%26rft.volume%253D16%26rft.spage%253D024045%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [32]: #xref-ref-8-1 "View reference 8 in text" [33]: #xref-ref-9-1 "View reference 9 in text" [34]: {openurl}?query=rft.jtitle%253DScience%2BAdvances%26rft.stitle%253DSci%2BAdv%26rft.aulast%253DStorlazzi%26rft.auinit1%253DC.%2BD.%26rft.volume%253D4%26rft.issue%253D4%26rft.spage%253Deaap9741%26rft.epage%253Deaap9741%26rft.atitle%253DMost%2Batolls%2Bwill%2Bbe%2Buninhabitable%2Bby%2Bthe%2Bmid-21st%2Bcentury%2Bbecause%2Bof%2Bsea-level%2Brise%2Bexacerbating%2Bwave-driven%2Bflooding%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fsciadv.aap9741%26rft_id%253Dinfo%253Apmid%252F29707635%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [35]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6MzoiUERGIjtzOjExOiJqb3VybmFsQ29kZSI7czo4OiJhZHZhbmNlcyI7czo1OiJyZXNpZCI7czoxMjoiNC80L2VhYXA5NzQxIjtzOjQ6ImF0b20iO3M6MjM6Ii9zY2kvMzcyLzY1NDgvMTI3OS5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30= [36]: #xref-ref-10-1 "View reference 10 in text" [37]: {openurl}?query=rft.jtitle%253DScience%2BAdvances%26rft.stitle%253DSci%2BAdv%26rft.aulast%253DRaymond%26rft.auinit1%253DC.%26rft.volume%253D6%26rft.issue%253D19%26rft.spage%253Deaaw1838%26rft.epage%253Deaaw1838%26rft.atitle%253DThe%2Bemergence%2Bof%2Bheat%2Band%2Bhumidity%2Btoo%2Bsevere%2Bfor%2Bhuman%2Btolerance%26rft_id%253Dinfo%253Adoi%252F10.1126%252Fsciadv.aaw1838%26rft_id%253Dinfo%253Apmid%252F32494693%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6MzoiUERGIjtzOjExOiJqb3VybmFsQ29kZSI7czo4OiJhZHZhbmNlcyI7czo1OiJyZXNpZCI7czoxMzoiNi8xOS9lYWF3MTgzOCI7czo0OiJhdG9tIjtzOjIzOiIvc2NpLzM3Mi82NTQ4LzEyNzkuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9 [39]: #xref-ref-11-1 "View reference 11 in text" [40]: {openurl}?query=rft.jtitle%253DEnviron.%2BRes.%2BLett.%26rft.volume%253D16%26rft.spage%253D044020%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [41]: #xref-ref-12-1 "View reference 12 in text" [42]: {openurl}?query=rft.jtitle%253DEnviron.%2BRes.%2BLett.%26rft.volume%253D15%26rft.spage%253D064003%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [43]: #xref-ref-13-1 "View reference 13 in text" [44]: #xref-ref-14-1 "View reference 14 in text" [45]: {openurl}?query=rft.jtitle%253DEnviron.%2BSci.%2BPolicy%26rft.volume%253D93%26rft.spage%253D129%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [46]: #xref-ref-15-1 "View reference 15 in text" [47]: {openurl}?query=rft.jtitle%253DNat.%2BFood%26rft.volume%253D2%26rft.spage%253D1%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx