Writing is involved in all aspects of graduate student research. Here we provide detailed guidance on writing for research, especially for master's theses and PhD dissertations and primarily for physical geography and other earth science students. The main sections of a research paper (Introduction, Literature Review, Methods, Results, Discussion, Conclusions) each have specific requirements of what to include and exclude. While there are many aspects to research writing, they should be thought of as a process leading to a final product. The proposal becomes a well-developed first draft of the Introduction, Literature Review, and Methods sections, with Results, Discussion, and Conclusions added after the data are collected and analyzed. New information and improvement of the writing lead to the frequent updating of all sections. In addition, Research Notes are fundamentally important to the process of research and students should develop the habit of entering and maintaining high-quality notes. The Supplement is where details are presented that are important but not included in the main text. References need to be taken seriously from the beginning of the project and the Abstract is the last major part to be written but must be carefully crafted.

This study utilized MODIS true color satellite imagery to analyse blowing sand and dust events dynamics in the Middle East from 2010 to 2021, focusing on Syria, Iraq, and Jordan. A total of 4923 dust point sources were detected, with a significant concentration (~90 %) located within the Tigris-Euphrates Basin (Nearest Neighbor Ratio = 0.41, р < 0.001). Land cover analysis revealed that bare land, comprising most of the study area, was the predominant source of dust emissions. Wetlands, though only constituting about 1 % of the area, showed the highest frequency of dust sources per unit area, highlighting their role as critical dust emission hotspots. The study emphasizes the impact of drought and anthropogenic factors, such as poor land management, on blowing dust intensity. It suggests the necessity of strategic land management practices, including re-vegetation of arid areas, reducing soil exposure, and implementing wind erosion control measures. To effectively address the transboundary nature of dust emissions, the findings underscore the importance of fostering regional cooperation through mechanisms such as shared environmental monitoring and data exchange platforms, joint management of cross-border natural resources, and collaborative policy making.

Drought affects the occurrence and intensity of aeolian dust events in many ecoregions. This study investigated the effects of "drought legacy" (the cumulative temporal persistence of drought's consequences on the earth system) on dust generation, a topic which had not previously been evaluated. We investigated the potential dust/drought legacy relationship in the USA's Southern Great Plains and Chihuahuan Desert ecoregions over 14 years, including the 2011-2012 extreme drought, at spatial distances up to 100 km around dust point sources and for up to 5 years' drought history. At every temporal and spatial scale, drought levels associated with dust initiation points in the two ecoregions were significantly different. Chihuahuan Desert dust sources concentrated in areas of severe to extreme drought, while those in the Southern Great Plains, which experiences greater land use, spanned wider ranges of drought conditions. At short temporal scales of drought (1 week and 1 year), dust initiation in the Chihuahuan Desert was associated strongly with severe to exceptional drought conditions at all spatial extents, while Southern Great Plains dust sources were linked to lower drought intensities. For longer drought histories (the past 2 and 5 years), Chihuahuan Desert dust sources were connected with moderate to severe drought, while Southern Great Plains dust sources were linked to moderate drought, at all spatial extents. Drought's legacies, extending over multiple years and kilometer scales, are shown to have roles in subsequent dust events at regional scales, though they can be regionally modulated by land use practices and ecosystem characteristics.

Establishing mineral dust impacts on Earth's systems requires numerical models of the dust cycle. Differences between dust optical depth (DOD) measurements and modelling the cycle of dust emission, atmospheric transport, and deposition of dust indicate large model uncertainty due partially to unrealistic model assumptions about dust emission frequency. Calibrating dust cycle models to DOD measurements typically in North Africa, are routinely used to reduce dust model magnitude. This calibration forces modelled dust emissions to match atmospheric DOD but may hide the correct magnitude and frequency of dust emission events at source, compensating biases in other modelled processes of the dust cycle. Therefore, it is essential to improve physically based dust emission modules. Here we use a global collation of satellite observations from previous studies of dust emission point source (DPS) dichotomous frequency data. We show that these DPS data have little-to-no relation with MODIS DOD frequency. We calibrate the albedo-based dust emission model using the frequency distribution of those DPS data. The global dust emission uncertainty constrained by DPS data (±3.8 kg m-2 y-1) provides a benchmark for dust emission model development. Our calibrated model results reveal much less global dust emission (29.1 ± 14.9 Tg y-1) than previous estimates, and show seasonally shifting dust emission predominance within and between hemispheres, as opposed to a persistent North African dust emission primacy widely interpreted from DOD measurements. Earth's largest dust emissions, proceed seasonally from East Asian deserts in boreal spring, to Middle Eastern and North African deserts in boreal summer and then Australian shrublands in boreal autumn-winter. This new analysis of dust emissions, from global sources of varying geochemical properties, have far-reaching implications for current and future dust-climate effects. For more reliable coupled representation of dust-climate projections, our findings suggest the need to re-evaluate dust cycle modelling and benefit from the albedo-based parameterisation.

Dust models are essential for understanding the impact of mineral dust on Earth's systems, human health, and global economies, but dust emission modelling has large uncertainties. Satellite observations of dust emission point sources (DPS) provide a valuable dichotomous inventory of regional dust emissions. We develop a framework for evaluating dust emission model performance using existing DPS data before routine calibration of dust models. To illustrate this framework's utility and arising insights, we evaluated the albedo-based dust emission model (AEM) with its areal (MODIS 500 m) estimates of soil surface wind friction velocity (us∗) and common, poorly constrained grain-scale entrainment threshold (u∗ts) adjusted by a function of soil moisture (H). The AEM simulations are reduced to its frequency of occurrence, P(us∗>u∗tsH). The spatio-temporal variability in observed dust emission frequency is described by the collation of nine existing DPS datasets. Observed dust emission occurs rarely, even in North Africa and the Middle East, where DPS frequency averages 1.8 %, (~7 days y-1), indicating extreme, large wind speed events. The AEM coincided with observed dust emission ~71.4 %, but simulated dust emission ~27.4 % when no dust emission was observed, while dust emission occurrence was over-estimated by up to 2 orders of magnitude. For estimates to match observations, results showed that grain-scale u∗ts needed restricted sediment supply and compatibility with areal us∗. Failure to predict dust emission during observed events, was due to us∗ being too small because reanalysis winds (ERA5-Land) were averaged across 11 km pixels, and inconsistent with us∗ across 0.5 km pixels representing local maxima. Assumed infinite sediment supply caused the AEM to simulate dust emission whenever P(us∗>u∗tsH), producing false positives when wind speeds were large. The dust emission model scales of existing parameterisations need harmonising and a new parameterisation for u∗ts is required to restrict sediment supply over space and time.

Large‐scale classical dust cycle models, developed more than two decades ago, assume for simplicity that the Earth's land surface is devoid of vegetation, reduce dust emission estimates using a vegetation cover complement, and calibrate estimates to observed atmospheric dust optical depth (DOD). Consequently, these models are expected to be valid for use with dust‐climate projections in Earth System Models. We reveal little spatial relation between DOD frequency and satellite observed dust emission from point sources (DPS) and a difference of up to 2 orders of magnitude. We compared DPS data to an exemplar traditional dust emission model (TEM) and the albedo‐based dust emission model (AEM) which represents aerodynamic roughness over space and time. Both models overestimated dust emission probability but showed strong spatial relations to DPS, suitable for calibration. Relative to the AEM calibrated to the DPS, the TEM overestimated large dust emission over vast vegetated areas and produced considerable false change in dust emission. It is difficult to avoid the conclusion that calibrating dust cycle models to DOD has hidden for more than two decades, these TEM modeling weaknesses. The AEM overcomes these weaknesses without using masks or vegetation cover data. Considerable potential therefore exists for ESMs driven by prognostic albedo, to reveal new insights of aerosol effects on, and responses to, contemporary and environmental change projections.

Abstract. Measurements of dust in the atmosphere have long been used to calibrate dust emission models. However, there is growing recognition that atmospheric dust confounds the magnitude and frequency of emission from dust sources and hides potential weaknesses in dust emission model formulation. In the satellite era, dichotomous (presence = 1 or absence = 0) observations of dust emission point sources (DPS) provide a valuable inventory of regional dust emission. We used these DPS data to develop an open and transparent framework to routinely evaluate dust emission model (development) performance using coincidence of simulated and observed dust emission (or lack of emission). To illustrate the utility of this framework, we evaluated the recently developed albedo-based dust emission model (AEM) which included the traditional entrainment threshold (u*ts) at the grain scale, fixed over space and static over time, with sediment supply infinite everywhere. For comparison with the dichotomous DPS data, we reduced the AEM simulations to its frequency of occurrence in which soil surface wind friction velocity (us*) exceeds the u*ts, P(us* > u*ts). We used a global collation of nine DPS datasets from established studies to describe the spatio-temporal variation of dust emission frequency. A total of 37,352 unique DPS locations were aggregated into 1,945 1° grid boxes to harmonise data across the studies which identified a total of 59,688 dust emissions. The DPS data alone revealed that dust emission does not usually recur at the same location, are rare (1.8 %) even in North Africa and the Middle East, indicative of extreme, large wind speed events. The AEM over-estimated the occurrence of dust emission by between 1 and 2 orders of magnitude. More diagnostically, the AEM simulations coincided with dichotomous observations ~71 % of the time but simulated dust emission ~27 % of the time when no dust emission was observed. Our analysis indicates that u*ts was typically too small, needed to vary over space and time, and at the grain-scale u*ts is incompatible with the us* scale (MODIS 500 m). During observed dust emission, us* was too small because wind speeds were too small and/or the wind speed scale (ERA5; 11 km) is incompatible with the us* scale. The absence of any limit to sediment supply caused the AEM to simulate dust emission whenever P (us* > u*ts), producing many false positives when and where wind speeds were frequently large. Dust emission model scaling needs to be reconciled and new parameterisations are required for u*ts and to restrict sediment supply varying over space and time. Whilst u*ts remains poorly constrained and unrealistic assumptions persist about sediment supply and availability, the DPS data provide a basis for the calibration of dust emission models for operational use. As dust emission models develop, these DPS data provide a consistent, reproducible, and valid framework for their routine evaluation and potential model optimisation. This work emphasises the growing recognition that dust emission models should not be evaluated against atmospheric dust.

Abstract. Dust emissions influence global climate while simultaneously reducing the productive potential and resilience of landscapes to climate stressors, together impacting food security and human health. Vegetation is a major control on dust emission because it extracts momentum from the wind and shelters the soil surface, protecting dry and loose material from erosion by winds. Many of the current dust emission models (TEM) assume that the Earth’s land surface is constantly devoid of vegetation, then adjust the dust emission using a vegetation cover reciprocal, and finally calibrate to dust in the atmosphere. We compare this approach with an albedo-based dust emission model (AEM) which calibrates Earth’s land surface shadow to shelter depending on wind speed, to represent aerodynamic roughness spatio-temporal variation. We also compare these dust emission models with estimates of dust in the atmosphere using dust optical depth frequency (DOD). Using existing datasets of satellite observed dust emission from dust point sources (DPS), we show that during the same period, DOD frequency exceeds DPS frequency by up to two orders of magnitude (RMSEDOD = 67 days). Relative to DPS frequency, both models over-estimated dust emission frequency by up to one order of magnitude (RMSETEM = 6 days; RMSEAEM = 4 days) but showed strong relations with DPS frequency suitable for calibrating models to observed dust emission. Theoretically, the TEM is incomplete in its formulation, which despite the pragmatic adjustment using the vegetation cover reciprocal, causes dust emission to be highly dependent on wind speed and over-estimates large (> 0.1 kg m−2 a−1) dust emission over vast vegetated areas. Consequently, the TEM produces considerable falsely positive change in dust emission, relative to the AEM. Since the main difference between the dust emission models is the treatment of aerodynamic roughness we conclude that its crude representation in the TEM has caused large, previously unknown, uncertainty in Earth System Models (ESMs). Our results indicate that tuning dust emission models to dust in the atmosphere has hidden for more than two decades, these TEM modelling weaknesses and its poor performance. The AEM overcomes these weaknesses and improves performance without tuning. In ESMs the AEM can be driven by available prognostic albedo to represent the fidelity of drag partition physics to reduce uncertainty of aerosol effects on, and responses to, contemporary and future environmental change.

Abstract NASA's ICESat‐2 mission measures Earth's elevation with the Advanced Topographic Laser Altimeter System (ATLAS), a 6‐beam photon‐counting laser altimeter. The Global Geolocated Photon data product (ATL03) is the primary source of photon information used by surface‐type‐specific higher‐level products, along with the Atmospheric Layer Characteristics product (ATL09). ATL03 provides time‐tagged, geolocated photon heights referenced to the ellipsoid and a parameter providing an initial classification of photon events as signal or background. We use this classification to evaluate ATLAS radiometry (number of signal photons per transmitted laser pulse) over short time scales and over the mission to date. The radiometric performance of ATLAS will in part determine what differences in the signal and background photon rates are significant and indicate geophysical, rather than instrumental, changes. We find the ATLAS radiometry is very stable over short time scales and exhibits a long‐term decrease of ∼1 signal photon per laser pulse (∼−12%) in the strong spots over the first 1.5 years of the mission.

Documenting geomorphology and land use/land cover associated with the aeolian dust sources is needed to build more robust models simulating wind erosion and dust emission. It is also important to set the base for future studies aiming to quantify the effect of anthropogenic land-use change on dust emission. In this research, dust point sources were detected in the Southern Great Plains and Chihuahuan Desert regions of the United States (U.S.) for 2001-2016, encompassing a period of extreme drought. Dust points were detected using overlay analysis of brightness temperature difference images of MODIS and MODIS true color images for the days where dust plumes were visible. This study aims to identify low emission surfaces as well as high emission surfaces in terms of geomorphology and land cover. A total of 1508 dust points were detected, where 1258 points are located in the Southern Great Plains, and 187 points are located in the Chihuahuan Desert. Point pattern analysis showed a significant cluster of these points in West Texas (Nearest Neighbor Ratio = 0.33, p < 0.001) where cultivated lands and grasslands are the dominant land cover and aeolian sand sheet is the dominant geomorphic class. Ephemeral lakes (i.e., playas) produce the most dust sources per unit area. Cultivated croplands enclose 43% of the dust points, while shrublands and grasslands, combined, enclose 45% of the points. Results from this study confirms the importance of playas as a dynamic source of dust in southwestern U.S. Moreover, this study suggests that anthropogenic factors play a major role in dust emission within southwestern U.S, although bare lands in particular that are not subjected to anthropogenic factors (e.g., the White Sands dune field) are also emitting dust. Future research is needed to statistically analyze the contribution of different land-cover types on dust emission in the region. (C) 2019 Elsevier B.V. All rights reserved.

Exploring the effects of drought and land use/land cover (LULC) on aeolian dust emission is important to enhance dust models to account for anthropogenic land surface change. Hitherto, there has been no systematic effort to quantitatively estimate associations between LULC and drought with the number of dust point sources as a surrogate for emission on both local and global levels. Previously, we created a dataset of dust emission point sources in the southwestern United States (U.S.) during the years 2001-2016, including a period of extreme drought. However, that work did not investigate the effects of drought on the detected dust point sources. Here, we used that dataset to test the hypothesis that there is a statistically significant association between drought level and LULC that may contribute to the number of dust point sources in the Southern Great Plains and Chihuahuan Desert regions of the U.S. The spatio-temporal analysis showed that the geographic mean center for all the dust points, as well as the majority of the annual geographic mean centers between 2001 and 2016, is located in the Southern High Plains. Areas suffering from severe to exceptional drought tend to attract the annual geographic mean center of dust points. The chi-square analysis results showed a significant association between land cover type (as defined in the National Land Cover Database) and drought level with the number of dust point sources (χ2 (6) = 45.54, р < 0.001), thus supporting the proposed hypothesis. Results from this study indicate that human activities in dust-prone regions have clear potential to worsen the negative impacts of drought by changing LULC and increasing erodibility in multiple ways. This study paves the way for future efforts that can utilize more data and conduct more robust statistical analysis of the drought-LULC-dust linkage on both regional and global scales.

A first look at data from NASA’s laser altimeter mission ICESat-2 reveals very high resolution 3-D profiles of ice on land and sea, forests, and shallow bodies of water.

The Ice, Cloud, and land Elevation Satellite - 2 (ICESat-2) observatory was launched on 15 September 2018 to measure ice sheet and glacier elevation change, sea ice freeboard, and enable the determination of the heights of Earth's forests. ICESat-2's laser altimeter, the Advanced Topographic Laser Altimeter System (ATLAS) uses green (532 nm) laser light and single-photon sensitive detection to measure time of flight and subsequently surface height along each of its six beams. In this paper, we describe the major components of ATLAS, including the transmitter, the receiver and the components of the timing system. We present the major components of the ICESat-2 observatory, including the Global Positioning System, star trackers and inertial measurement unit. The ICESat-2 Level 1B data product (ATL02) provides the precise photon round-trip time of flight, among other data. The ICESat-2 Level 2A data product (ATL03) combines the photon times of flight with the observatory position and attitude to determine the geodetic location (i.e. the latitude, longitude and height) of the ground bounce point of photons detected by ATLAS. The ATL03 data product is used by higher-level (Level 3A) surface-specific data products to determine glacier and ice sheet height, sea ice freeboard, vegetation canopy height, ocean surface topography, and inland water body height.

Quantifying the effect of anthropogenic land use change on dust emission is a contentious issue. In this research, 1508 dust point sources were detected in the Southern High Plains and Chihuahuan Desert regions of the United States for 2001-2016, encompassing a period of extreme drought. These points were subjected to quantitative and spatio-temporal analysis. Point pattern analysis showed a significant cluster of these points in West Texas (Nearest Neighbor Ratio = 0.33, р < 0.001) where cultivated lands and grasslands are dominant land cover. Spatial observation suggests that the geographic center of dust points in these regions shifts away from bare soil and shrublands toward grasslands and cultivated lands as drought level increases, while it shifts away from grasslands and cultivated lands towards bare soil and shrublands in cases of no drought. Chi-square test captured a significant association between land use type and drought level on dust emission (χ2 (6) = 47.33, р < 0.001). However, Cramer’s V value (0.13, р < 0.001) indicates that the association captured by the chi-square is weak, suggesting that other factors, perhaps meteorological variables, are at play in the spatial distribution of dust sources in this region. The proportion of dust points differs significantly during severe/exceptional droughts versus no drought or abnormally dry/moderate drought in both cultivated lands and grasslands. The proportion of dust points in bare lands and shrublands, however, did not significantly change between no drought and severe-exceptional drought. These results suggest anthropogenic land use in southwestern U.S. is significantly associated with drought in terms of dust emission. Human activity amplifies of the effects of drought by increasing soil erodibility; thus, adopting land management practices to resist wind erosion is crucial. Further investigations on a global scale should provide more information on this association.

This editorial represents a clarion call for the aeolian research community to provide increased scientific input to the Intergovernmental Panel on Climate Change (IPCC) and the United Nations Convention to Combat Desertification (UNCCD) and an invitation to apply for ISAR funding to organize a working group to support this engagement.