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As climate change and extreme weather events increasingly threaten traditional landscapes and livelihoods of entire communities the need to study its impact on human migration and population displacement has never been greater. The Atlas of Environmental Migration is the first illustrated publication mapping this complex phenomenon. It clarifies terminology and concepts, draws a typology of migration related to environment and climate change, describes the multiple factors at play, explains the challenges, and highlights the opportunities related to this phenomenon. Through elaborate maps, diagrams, illustrations, case studies from all over the world based on the most updated international research findings, the Atlas guides the reader from the roots of environmental migration through to governance. In addition to the primary audience of students and scholars of environment studies, climate change, geography and migration it will also be of interest to researchers and students in politics, economics and international relations departments.

With three roads and a population of just over 500 people, Shishmaref, Alaska seems like an unlikely center of the climate change debate. But the island, home to Iñupiaq Eskimos who still live off subsistence harvesting, is falling into the sea, and climate change is, at least in part, to blame. While countries sputter and stall over taking environmental action, Shishmaref is out of time. Publications from the New York Times to Esquire have covered this disappearing village, yet few have taken the time to truly show the community and the two millennia of traditions at risk. In Fierce Climate, Sacred Ground , Elizabeth Marino brings Shishmaref into sharp focus as a place where people in a close-knit, determined community are confronting the realities of our changing planet every day. She shows how physical dangers challenge lives, while the stress and uncertainty challenge culture and identity. Marino also draws on Shishmaref's experiences to show how disasters and the outcomes of climate change often fall heaviest on those already burdened with other social risks and often to communities who have contributed least to the problem. Stirring and sobering, Fierce Climate, Sacred Ground proves that the consequences of unchecked climate change are anything but theoretical.

Increasing connectivity is an important strategy for facilitating species range shifts and maintaining biodiversity in the face of climate change. To date, however, few researchers have included future climate projections in efforts to prioritize areas for increasing connectivity. We identified key areas likely to facilitate climate-induced species' movement across western North America. Using historical climate data sets and future climate projections, we mapped potential species' movement routes that link current climate conditions to analogous climate conditions in the future (i.e., future climate analogs) with a novel moving-window analysis based on electrical circuit theory. In addition to tracing shifting climates, the approach accounted for landscape permeability and empirically derived species' dispersal capabilities. We compared connectivity maps generated with our climate-hange-informed approach with maps of connectivity based solely on the degree of human modification of the landscape. Including future climate projections in connectivity models substantially shifted and constrained priority areas for movement to a smaller proportion of the landscape than when climate projections were not considered. Potential movement, measured as current flow, decreased in all ecoregions when climate projections were included, particularly when dispersal was limited, which made climate analogs inaccessible. Many areas emerged as important for connectivity only when climate change was modeled in 2 time steps rather than in a single time step. Our results illustrate that movement routes needed to track changing climatic conditions may differ from those that connect present-day landscapes. Incorporating future climate projections into connectivity modeling is an important step toward facilitating successful species movement and population persistence in a changing climate. Incrementar la conectividad es una estrategia importante para facilitarle a las especies cambios en su extensión y mantener a la biodiversidad de frente al cambio climático. Sin embargo, a la fecha pocos investigadores ban incluido las proyecciones del futuro climático en los esfuerzos priorizar áreas para incrementar la conectividad. Identificamos áreas clave con probabilidad de facilitar el movimiento de las especies inducido por el clima en América del Norte. Por medio de un análisis novedoso de ventana en movimiento basado en la teoría de los circuitos eléctricos, mapeamos las rutas potenciales de movimiento de las especies que enlazan las condiciones climáticas actuates con condiciones climáticas análogas en el futuro (es decir, futuros climas análogos) utilizando conjuntos de datos históricos del clima y las proyecciones del futuro climático. Además de rastrear los climas cambiantes, la estrategia tomó en cuenta la permeabilidad del paisaje y derivó empíricamente las capacidades de dispersión de las especies. Comparamos los mapas de conectividad generados con nuestra estrategia informada por el cambio climático con los mapas de conectividad basados solamente en el grado de modificación humana del paisaje. La inclusión de las proyecciones del futuro climático dentro de los modelos de conectividad modificó y restringió sustancialmente las áreas prioritarias de movimiento a una porción mas pequeña del paisaje que cuando no se consideraron las proyecciones climàticas. El movimiento potential, medido como el flujo de corriente, disminuyó en todas las ecoregiones cuando se incluyeron las proyecciones climáticas, particularmente cuando la dispersión estuvo limitada, lo que hizo que los análogos climáticos fueran inaccesibles. Muchas áreas emergieron como importantes para la conectividad sólo cuando el cambio climático fue modelado en pasos de dos tiempos, en lugar de un paso de un sólo tiempo. Nuestros resultados ilustran que las rutas de movimiento necesarias para rastrear las condiciones climáticas cambiantes pueden diferir de aquellas que conectan a los paisajes hoy en día. La incorporatión de las proyecciones del futuro climático dentro del modelado de conectividad es un paso importante hacia la facilitatión del movimiento exitoso para las especies y la persistencia de las poblaciones en un clima cambiante.

Aedes aegypti and Ae. albopictus are the primary vectors that transmit several arboviral diseases, including dengue, chikungunya, and Zika. The world is presently experiencing a series of outbreaks of these diseases, so, we still require to better understand the current distributions and possible future shifts of their vectors for successful surveillance and control programs. Few studies assessed the influences of climate change on the spatial distributional patterns and abundance of these important vectors, particularly using the most recent climatic scenarios. Here, we updated the current potential distributions of both vectors and assessed their distributional changes under future climate conditions. We used ecological niche modeling approach to estimate the potential distributions of Ae. aegypti and Ae. albopictus under present-day and future climate conditions. This approach fits ecological niche model from occurrence records of each species and environmental variables. For each species, future projections were based on climatic data from 9 general circulation models (GCMs) for each representative concentration pathway (RCP) in each time period, with a total of 72 combinations in four RCPs in 2050 and 2070. All ENMs were tested using the partial receiver operating characteristic (pROC) and a set of 2,048 and 2,003 additional independent records for Ae. aegypti and Ae. albopictus, respectively. Finally, we used background similarity test to assess the similarity between the ENMs of Ae. aegypti and Ae. albopictus. The predicted potential distribution of Ae. aegypti and Ae. albopictus coincided with the current and historical known distributions of both species. Aedes aegypti showed a markedly broader distributional potential across tropical and subtropical regions than Ae. albopictus. Interestingly, Ae. albopictus was markedly broader in distributional potential across temperate Europe and the United States. All ecological niche models (ENMs) were statistically robust (P < 0.001). ENMs successfully anticipated 98% (1,999/2,048) and 99% (1,985/2,003) of additional independent records for both Ae. aegypti and Ae. albopictus, respectively (P < 0.001). ENMs based on future conditions showed similarity between the overall distributional patterns of future-day and present-day conditions; however, there was a northern range expansion in the continental USA to include parts of Southern Canada in case of Ae. albopictus in both 2050 and 2070. Future models also anticipated further expansion of Ae. albopictus to the East to include most of Europe in both time periods. Aedes aegypti was anticipated to expand to the South in East Australia in 2050 and 2070. The predictions showed differences in distributional potential of both species between diverse RCPs in 2050 and 2070. Finally, the background similarity test comparing the ENMs of Ae. aegypti and Ae. albopictus was unable to reject the null hypothesis of niche similarity between both species (P > 0.05). These updated maps provided details to better guide surveillance and control programs of Ae. aegypti and Ae. albopictus. They have also significant public health importance as a baseline for predicting the emergence of arboviral diseases transmitted by both vectors in new areas across the world.

Land cover (LC) determines the energy exchange, water and carbon cycle between Earth's spheres. Accurate LC information is a fundamental parameter for the environment and climate studies. Considering that the LC in China has been altered dramatically with the economic development in the past few decades, sequential and fine-scale LC monitoring is in urgent need. However, currently, fine-resolution annual LC dataset produced by the observational images is generally unavailable for China due to the lack of sufficient training samples and computational capabilities. To deal with this issue, we produced the first Landsat-derived annual China land cover dataset (CLCD) on the Google Earth Engine (GEE) platform, which contains 30 m annual LC and its dynamics in China from 1990 to 2019. We first collected the training samples by combining stable samples extracted from China's land-use/cover datasets (CLUDs) and visually interpreted samples from satellite time-series data, Google Earth and Google Maps. Using 335 709 Landsat images on the GEE, several temporal metrics were constructed and fed to the random forest classifier to obtain classification results. We then proposed a post-processing method incorporating spatial–temporal filtering and logical reasoning to further improve the spatial–temporal consistency of CLCD. Finally, the overall accuracy of CLCD reached 79.31 % based on 5463 visually interpreted samples. A further assessment based on 5131 third-party test samples showed that the overall accuracy of CLCD outperforms that of MCD12Q1, ESACCI_LC, FROM_GLC and GlobeLand30. Besides, we intercompared the CLCD with several Landsat-derived thematic products, which exhibited good consistencies with the Global Forest Change, the Global Surface Water, and three impervious surface products. Based on the CLCD, the trends and patterns of China's LC changes during 1985 and 2019 were revealed, such as expansion of impervious surface (+148.71 %) and water (+18.39 %), decrease in cropland (−4.85 %) and grassland (−3.29 %), and increase in forest (+4.34 %). In general, CLCD reflected the rapid urbanization and a series of ecological projects (e.g. Gain for Green) in China and revealed the anthropogenic implications on LC under the condition of climate change, signifying its potential application in the global change research. The CLCD dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.4417810 (Yang and Huang, 2021).

Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1∘ at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling–Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions to the identification of hydroclimatic variability and change and as raw input for post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview (last access: 28 June 2020) with the following DOI: https://doi.org/10.24381/cds.a4fdd6b9 (C3S, 2019).

Endemic mycoses such as histoplasmosis, coccidioidomycosis, blastomycosis, paracoccidioidomycosis, and talaromycosis are well-known causes of focal and systemic disease within specific geographic areas of known endemicity. However, over the past few decades, there have been increasingly frequent reports of infections due to endemic fungi in areas previously thought to be “non-endemic.” There are numerous potential reasons for this shift such as increased use of immune suppressive medications, improved diagnostic tests, increased disease recognition, and global factors such as migration, increased travel, and climate change. Regardless of the causes, it has become evident that our previous understanding of endemic regions for these fungal diseases needs to evolve. The epidemiology of the newly described Emergomyces is incomplete; our understanding of it continues to evolve. This review will focus on the evidence underlying the established areas of endemicity for these mycoses as well as new data and reports from medical literature that support the re-thinking these geographic boundaries. Updating the endemic fungi maps would inform clinical practice and global surveillance of these diseases.

The impacts of global warming/climate change on extreme rainfall events during the Baiu period in Japan and their dependency on weather patterns (WPs) were examined using the self-organizing map (SOM) method. To investigate the differences in climate change impacts on daily rainfall among the WPs, a SOM was applied to surface atmospheric circulation data from the Database for Policy Decision Making for Future Climate Change (d4PDF) to determine the dominant heavy rainfall WPs. The obtained SOM shows that different WPs are associated with regional variations in extreme rainfall events. Projected future changes in the occurrence of heavy rainfall displayed a non-uniform spatial distribution. To understand the spatial heterogeneous rainfall changes, the sensitivity of heavy rainfall WPs to climate forcing was evaluated. Results of the SOM analysis suggest that this regional variation in the future changes in extreme rainfall events could be attributed to sensitivity differences between WPs to the climate changes. These sensitivity differences can be attributed to the non-uniform spatial changes in the large-scale climatological background state in East Asia via modulations in the moist air intrusion into Japan.

Reliable drought projections are crucial for the effective managements of future drought risk. Most of the existing drought projections over Southern Africa are based on precipitation alone, neglecting the influence of potential evapotranspiration (PET). The present study shows that inclusion of PET may alter the magnitude and robustness of the drought projections. The study used two drought indices to project potential impacts of global warming on Southern African droughts, focusing on four major river basins. One of the drought indices (SPEI: Standardized Precipitation Evapotranspiration Index) is obtained from climate water balance (i.e. precipitation minus potential evapotranspiration) while the other (SPI: Standardized Precipitation Index) is calculated from precipitation alone. For the projections, we analyzed multi-model regional climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) at four specific global warming levels (GWLs) (i.e., 1.5 °C, 2.0 °C, 2.5 °C, and 3.0 °C) above the pre-industrial level and used the self-organizing maps to classify the drought projections into groups based on their similarities. Our results show that the CORDEX simulations give a realistic representation of all the necessary climate variables for quantifying droughts over Southern Africa. The simulations project a robust increase in SPEI drought intensity and frequency over Southern Africa and indicate that the magnitude of the projection increases with increasing GWLs, especially over the various river basins. In contrast, they project a non-significant change in SPI droughts at all the GWLs. The majority of the simulations clearly distinguish between the projected SPEI and SPI drought patterns, and the distinction becomes clearer with increasing GWLs. Hence, using precipitation alone for drought projection over Southern Africa may underestimate the magnitude and robustness of the projections. This study has application in mitigating climate change impacts on drought risk over Southern African river basins in the future.

Operational global-scale hydrological forecasting systems are used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/, last access: 3 December 2022) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the continuous ranked probability skill score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93 % of catchments in the short (1 to 3 d) and medium range (5 to 15 d) against a persistence benchmark forecast and skilful in over 80 % of catchments out to the extended range (16 to 30 d) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score available as a new layer on the GloFAS forecast Web Map Viewer to aid user interpretation and understanding of forecast quality.