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NATURE PODCAST
09 February 2022

RNA test detects deadly pregnancy disorder early

RNA in blood reveals signs of pre-eclampsia before symptoms occur, and the issue of urine in our sewage and what can be done about it.

Nick Petrić Howe

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Shamini Bundell

Nick Petrić Howe

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In this episode:00:46 Predicting pre-eclampsiaCell-free RNA circulates in the blood and can give clues as to what is going on in the body. This can be used to detect disease before symptoms occur. Now researchers have analysed cell-free RNA in pregnant people and have found it can give early warning signs of a serious, and sometimes fatal, disorder of pregnancy — pre-eclampsia.Research Article: Moufarrej et al.07:19 Research HighlightsUpgrading machine vision by modelling it on human eyes, and stacked skeletons which could show attempts at repair after European tomb raiders.Research Highlight: Retina-like sensors give machines better visionResearch Highlight: ‘Spines on posts’ hint at ancient devotion to the dead09:55 The problems of peeSewage and the way it is managed can cause serious problems, for example contaminants in waste can lead to harmful algal blooms. One of the major causes of this is urine, and so some researchers have been promoting a deceptively simple solution — separate out the urine.News Feature: The urine revolution: how recycling pee could help to save the worldBook Review: Toilets – what will it take to fix them?16:40 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, how China has planned to make this year’s Winter Olympics carbon neutral, and how a new radio telescope in Namibia will help us understand black holes.Nature News: China’s Winter Olympics are carbon-neutral — how?Nature News: Major African radio telescope will help to image black holesSubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed.

doi: https://doi.org/10.1038/d41586-022-00390-2

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Identifying SDGs related to nutrient pollution in Chinese water systemsThe SDGs (and their targets) that are relevant to nutrient pollution in Chinese water systems are identified based on the existing literature (Supplementary Table 1) and expert judgments. The targets of the 17 SDGs are officially listed as one-sentence statements that guide SDG implementation. Based on these one-sentence statements, we identify keywords for each SDG target (Supplementary Table 5). In this way, the potential link between a target and nutrient pollution is investigated by performing a keyword search in the existing literature. The keywords for each target are compared to different keywords related to nutrient pollution, such as “nutrient pollution”, “nutrient management”, or “water quality”, to span the array of academic literature that potentially exists on the subject. Additional keywords such as “China” or “Chinese waters” are added to the query, so the literature review is made specific to the national or regional context. In the case where no specific information is found, information is then extrapolated from global studies (Supplementary Table 1). We define three levels of the relevance of targets to nutrient pollution: “high”, “moderate” and “low”. The level referred to as “high” encompasses the targets that address the direct sources of nutrient losses to the Chinese water systems. The targets identified as being of “moderate” relevance comprise factors that address the impacts of nutrient pollution on aquatic ecosystems and human health or influence the resilience of ecosystems to nutrient pollution. The targets of “low” relevance cover technological, social, administrative or economic interventions indirectly related to nutrient pollution in water systems. The targets identified as “high”, “moderate” and “low” are listed in Fig. 1 and Supplementary Table 1.Assessing synergies and tradeoffs between SDGs 6 and 14 and other SDGsThe target-level interactions between SDGs relevant to nutrient pollution in inland freshwater and coastal waters in China are assessed. More specifically, an innovative aspect of this research lies in its evaluation of the potential positive (synergetic) and negative (tradeoff) interactions existing between SDG targets in the context of nutrient pollution in Chinese water systems. These interactions are assessed based on the existing literature (Supplementary Table 1) and our expert judgments on water pollution in China using the seven-point-scale framework of Griggs et al.25, who classified the interactions at 7 levels: (−3) canceling, (−2) counteracting, (−1) constraining, (0) consistent, (+1) enabling, (+2) reinforcing, and (+3) indivisible. The negative levels refer to tradeoffs, while the positive levels refer to synergies. Zero refers to a neutral relationship between targets. The definitions of the 7 levels of interactions are summarized in Supplementary Table 2.The identified interactions have a direction, either unidirectional (one direction) or bidirectional (two directions). A unidirectional interaction means that target A affects target B, but target B does not affect target A, whereas a bidirectional interaction means that target A affects target B, and target B also affects target A. An example of a unidirectional interaction is the tradeoff between targets 2.3 and 6.4: water use in intensive agriculture to meet target 2.3, aiming to double agricultural productivity, may counteract reducing water scarcity to meet target 6.4. An example of bidirectional interaction is the synergy between targets 11.6 and 6.3: reducing water pollution in cities by improving wastewater management to meet target 11.6 is indivisible from improving water quality by halving the proportion of untreated wastewater to meet target 6.3, and vice versa. The identified interactions and their directions are illustrated in Figs. 2 and 3 and explained in Supplementary Tables 3, 4. We realize that such an assessment of the interactions can differ among experts and therefore require continuous iterations and improvements. The interactions that we identified, however, provide a primary and good basis for such continuous effort that contributes to understanding how SDGs are interrelated in the context of water pollution in China.ScenariosWe explore future (1 baseline + 5 alternative) scenarios to achieve the SDGs for improved river and coastal water quality in China using the MARINA 2.0 model. Our five alternative scenarios are developed to reduce water pollution while benefitting agriculture, sewage, food consumption, and climate mitigation by accounting for the interactions between the SDGs. We account for synergies and tradeoffs in developing these scenarios through the following steps. First, we make an inventory of the measures that are effective in reducing nutrient pollution in Chinese water systems based on existing scenario analyses52,53,54,55. Next, based on the identified SDG interactions, we identify the measures that contribute to achieving SDGs 6 and 14 as well as SDGs 2, 11, 12, and 13 simultaneously. In other words, we try to include in our scenarios only the measures that promote synergies and avoid tradeoffs between SDGs 6 and 14 and SDGs 2, 11, 12, and 13. For example, agricultural practices and technologies to improve nutrient use efficiencies are adopted in the alternative scenarios, which reduces nutrient losses to waters from agriculture for SDGs 6 and 14 while maintaining food production for SDG 2 (synergies between SDGs). Measures to control water pollution, such as reducing fertilizer use, which may result in yield losses, are thus not considered, as they can lead to challenges in achieving SDG 2 (tradeoffs between SDGs). In other words, the five alternative scenarios are developed based on measures of action promoting the synergies and mitigating the tradeoffs between key SDGs (i.e., water, agriculture, sewage, food consumption, and climate mitigation) (Supplementary Table 6). The interactions (synergies and tradeoffs) addressed by each specific assumption in the alternative scenarios are presented in Supplementary Table 7 and Supplementary Figs. 3–7.The baseline SSP5-RCP8.5 scenario assumes relatively low population growth, fast economic growth, high fossil fuel consumption, and high international trade, increasing productivity in agriculture and environmental policies for local issues16,56,57. As a result, in 2050, sewage systems will be slightly improved compared to those today. Not all wastewater will be connected to sewage systems, especially in rural areas, where only 10% of wastewater will be collected (Supplementary Table 6). Nutrient removal during treatment will remain low or moderate at ~12–47% for N and 44–75% for P in rural and urban areas (Supplementary Table 6). Crops will be produced with fewer resources (e.g., nutrients, land, and water) because of increased productivity. Animal production will be intensive and industrialized to meet the increasing preference for meat-rich diets. Improved manure management is implemented to reduce emissions of NH3 and N2O during manure storage and housing. A total of 15–41% of crop residues and 70% of animal manure will be recycled in agriculture (Supplementary Table 6). The remainder will be lost to the environment. The import of food for consumption will be 17% higher in 2050 than in 2012 (Supplementary Table 6). The greenhouse gas (GHG) emissions of China, as well as those of other countries, will be high due to high fossil fuel consumption.The SE (improved sewage treatment) scenario builds on the SSP5-RCP8.5 and assumes further improved sewage systems by 2050 based on the targets of SDG 11 “Sustainable Cities and Communities”. According to current Chinese policies, wastewater connected to sewage systems will reach 70–95% in urban areas and 60% in one-third of China’s counties, including rural and urban areas, by 2050 (Supplementary Table 6). We, therefore, assume in this scenario that by 2050, all wastewater will be connected to centralized (in urban areas) or decentralized (in rural areas) sewage systems, following Strokal et al.52 Nutrient removal during treatment is assumed to reach 80% for N and 90% for P by adopting the best treatment technologies22,52 (Supplementary Table 6). These scenario assumptions promote 9 synergies and mitigate 3 tradeoffs between SDGs for clean water (SDGs 6 and 14) and SDG 11 (Supplementary Fig. 3).The AG (improved nutrient use efficiencies in agriculture) scenario builds on the SSP5-RCP8.5 and assumes further improved nutrient use efficiencies in agriculture by 2050 based on the targets of SDG 2 “Zero Hunger”. In this scenario, crops will be fertilized according to their needs for nutrients based on a balanced fertilization approach53,54. As a result, the use of synthetic fertilizers will be largely reduced compared to the baseline, without yield loss. Recycling up to 80% of straw residues on cropland will largely reduce air pollution due to straw burning (Supplementary Table 6). Animal production will be more efficient by using improved animal feeding and genetically modified animals that use nutrients more efficiently58. In the AG scenario, N and P excretions are thus 12% lower than in the baseline SSP5-RCP8.5 (Supplementary Table 6). Improved manure management is incorporated to reduce NH3 and N2O emissions during manure storage59,60,61. In the AG scenario, the direct discharge of manure will be restricted by policies; thus, all manure is assumed to be treated and recycled on cropland. These scenario assumptions promote 8 synergies and mitigate 10 tradeoffs between SDGs for clean water (SDG 6 and 14) and SDG 2 (Supplementary Fig. 4).The AG + SE scenario combines the storylines of the SE and AG scenarios that are developed based on SDGs 2 and 11. The AG + SE scenario assumes improved sewage systems and nutrient use efficiencies in agriculture. This scenario will promote 17 synergies and mitigate 13 tradeoffs between SDGs for clean water (SDG 6 and 14) and SDGs 2 and 11 (Supplementary Fig. 5).The AG + SE + SFC (sustainable food consumption in addition to AG + SE) scenario builds on AG + SE scenario and assumes additionally healthier diets and less food waste by 2050 based on the targets of SDG 12 “Responsible Consumption and Production”. In this scenario, society will follow Chinese dietary guidelines (CDGs)62, which recommend consuming less meat and more milk, eggs, vegetables, and fruits. Food waste will be reduced by 20% through responsible consumption, improved food processing, and storage facilities55. The reduction in meat consumption and food waste will result in a 20% reduction in the requirements for crop and animal production. China may remain a large importer of soybean due to limited land resources and increasing food demand63. For soybeans, we assume that approximately 80% of the soybean consumption in 2050 will be imported from abroad, following the assumption in Ma et al.55 In addition to the above assumptions, this scenario assumes the further improved management of animal manure. In the AG + SE scenario, many river basins do not have enough arable land to recycle all the manure produced in the basin. Therefore, the AG + SE + SFC scenario assumes that the excessive manure will be either treated (as effectively as wastewater) or exported to other regions in China to be recycled. Finally, atmospheric N deposition is assumed to be reduced by 50% relative to that in the SSP5-RCP8.5 by reducing NH3 and nitrogen oxide (NOx) emissions in the agricultural and nonagricultural sectors (e.g., controlling NH3 and NOx emissions from industries). These scenario assumptions promote 42 synergies and mitigate 13 tradeoffs between SDGs for clean water (SDGs 6 and 14) and SDGs 2, 11, and 12 (Supplementary Fig. 6).The AG + SE + SFC + CLI (climate mitigation in addition to AG + SE + SFC) scenario builds on the AG + SE + SFC scenario and additionally assumes a global effort in climate mitigation by 2050 based on the targets of SDG 13 “Climate Action”. In an earlier study using the MARINA 2.0 model16, the baseline SSP5-RCP8.5 scenario assumes high GHG concentrations under higher fossil fuel consumption, which will lead to considerable climate change and thus affect hydrology (e.g., river discharge). The AG + SE + SFC + CLI scenario assumes that GHG emissions will be reduced to the level of the RCP2.6 scenario by 2050, which implies efforts by countries worldwide to reduce GHG emissions to achieve Paris Agreement temperature targets64. The lower GHG emissions in the future may result in fewer increases in precipitation and river discharge than in the baseline, thus lessening the decrease in the in-river retention of nutrients. The river export of nutrients may thus be reduced by climate mitigation in this scenario compared to the baseline. These scenario assumptions promote 56 synergies and mitigate 13 tradeoffs between SDGs for clean water (SDGs 6 and 14) and SDGs 2, 11, 12, and 13 (Supplementary Fig. 7).MARINA 2.0 modelWe use the MARINA 2.0 model16 to explore future nutrient pollution in the rivers and coastal waters of China. This model is developed to quantify the river export of TDN and TDP in four forms by rivers at the subbasin scale from different sources16. The four nutrient forms are dissolved inorganic N (DIN), dissolved organic N (DON), dissolved inorganic P (DIP), and dissolved organic P (DOP). TDN is the sum of DIN and DON, and TDP is the sum of DIP and DOP.The MARINA 2.0 model quantifies the river export of TDN and TDP as a function of N and P inputs to surface waters (rivers) from diffuse and point sources and retentions of N and P in rivers based on Eq. 1, respectively16,29:$${M}_{F.y.j}=(RSdi{f}_{F.y.j}+RSpn{t}_{F.y.j})cdot F{E}_{riv.F.outlet.j}cdot F{E}_{riv.F.mouth.j}$$
(1)
where MF.y.j is the river export of N and P in form F (DIN, DON, DIP, DOP) by source y from subbasin j (kg year-1). RSdifF.y.j is the N and P inputs in form F to rivers (surface waters) from diffuse sources y in subbasin j (kg year−1). RSpntF.y.j is the N and P inputs in form F to rivers from point sources y in subbasin j (kg year−1). FEriv.F.outlet.j is the fraction of N and P in form F exported to the outlet of subbasin j (0–1). FEriv.F.mouth.j is the fraction of N and P in form F exported from the outlet of subbasin j to the river mouth (0–1). The detailed equations to quantify RSdifF.y.j, RSpntF.y.j, FEriv.F.outlet.j and FEriv.F.mouth.j are available in the SI of Wang et al.16.We model nutrient pollution in the rivers and coastal waters of six large rivers in China (Supplementary Fig. 1). These rivers include the Liao, Hai, and Yellow Rivers draining into the Bohai Gulf; the Huai River draining into the Yellow Sea; the Yangtze River draining into the East China Sea; and the Pearl River draining into the South China Sea. We select these rivers because they contribute largely to nutrient pollution in the coastal waters of China. According to Wang et al.16, these six rivers contributed ~90% to the river export of TDN and TDP to the Chinese seas in 2012. The drainage basins of the Yellow, Yangtze, and Pearl Rivers are divided into upstream, middle-stream and downstream subbasins, respectively, following Wang et al.16 The names of the subbasins are available in Supplementary Fig. 2.Indicators for SDGs 6 and 14Two indicators are calculated from the MARINA 2.0 model results to assess whether SDGs 6 and 14 are met. We use water quality standards for N and P concentrations as the indicator for SDG 6 and the Indicator for Coastal Eutrophication Potential (ICEP) for SDG14. Below, we describe how these indicators are chosen based on the UN-defined indicators and how they are calculated.The goal of SDG 6 is to “ensure the availability and sustainable management of water and sanitation for all”65. One important indicator for assessing SDG 6 is the “6.3.2 proportion of bodies of water with good ambient water quality”, according to the global indicator list from the UN66. In this study, we take an indicator for “good ambient water quality” from the Chinese “Environmental Quality Standard for Surface Water”23. This standard was adopted by “China’s National Plan on Implementation of the 2030 Agenda for Sustainable Development” to achieve SDG 618. China developed this plan to translate each target of the SDGs into “action plans”, considering opportunities and challenges that it faces in implementing the 2030 Agenda. According to the Chinese “Environmental Quality Standard for Surface Water”, “third grade” (grade III) refers to good ambient water quality23. For “grade-III” water in rivers, the concentration of NH3 may not exceed 1.0 mg-N/L, and that of total P (TP) may not exceed 0.2 mg-P/L. The MARINA 2.0 model quantifies DIN (including NH3, NO3−, and NO2) and TDP but not NH3 and TP. Therefore, we calculate N and P concentrations at the outlets of subbasins using modeled DIN and TDP loads and river discharges at the outlets. We compare the calculated concentrations of DIN and TDP with the water quality standards for “grade-III” water and discuss whether our scenarios contribute to the achievement of SDG 6.The goal of SDG 14 is to “conserve and sustainably use the oceans, seas and marine resources for sustainable development”65. The UN’s global indicator list suggests “14.1.1 Index of Coastal Eutrophication” as an indicator for this SDG66. Therefore, we take ICEP as an indicator for assessing the potential of coastal eutrophication for SDG 14, as it indicates the potential for the new production of harmful algae in coastal waters. This indicator is calculated by comparing the N, P, and silica (Si) loads and the Redfield molar ratios (C:N:P:Si ratios: 106:16:1:20) (see Garnier et al.43 for the detailed approach to quantifying the ICEP). Positive ICEP values indicate relatively high potentials for harmful algal blooms when rivers deliver excess N or P over Si to the sea. Negative ICEP values indicate relatively low potentials for harmful algal blooms. We calculate the ICEP values for the six Chinese rivers using the modeled river export of TDN and TDP from the MARINA 2.0 model. Based on the results, we discuss whether our scenarios contribute to the achievement of SDG 14. More

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MeteorologyWe reconstruct the meteorology at the South Col using observations from the automatic weather station (AWS) there (at 7945 m a.s.l)5 to downscale ERA5 reanalysis via a parsimonious blend of bias correction and machine learning. Initial screening indicates strong correlations between hourly ERA5 pressure level data bilinearly interpolated to South Col and air temperatures (r = 0.98), wind speed (r = 0.94), and relative humidity (r = 0.80) observed by the AWS. We therefore apply a simple empirical quantile mapping correction29 to remove systematic bias for these variables.Incident shortwave (SW) and longwave (LW) radiation are not available on ERA5 pressure levels, so we reconstruct them by downscaling the transmissivity (τ) and emissivity (α) of the atmosphere, defined:$$tau = {{{mathrm{SW/}}}}{Psi}$$
(1)
$${{{mathrm{and}}}};alpha = {{{mathrm{LW/}}}}sigma T_{{{mathrm{a}}}}^4$$
(2)
Where Ψ is the theoretical top of atmosphere solar radiation, σ is the Stefan Boltzmann constant (5.67 × 10−8 W m−2 K−4), and Ta is the 2 m air temperature (Kelvin). Observed values for τ and α are evaluated using AWS measurements of incident radiation and air temperature (using calculations of solar geometry to compute Ψ). We then train a Random Forest model (with 100 trees and a minimum leaf size of three) using Python’s Scikit Learn (version 0.20.1), modeling τ and α as a function of the ERA5 predictors in Methods Table 1. SW and LW could then be computed from:$${{{mathrm{SW}}}} = tau {Psi}$$
(3)
$${{{mathrm{LW}}}} = varepsilon T^4$$
(4)
Where T is the estimate from the bias-corrected ERA5 data.Table 1 Predictor variables used in the machine learning downscaling.Full size tableWe calibrate the bias correction and RF models using between 5012 (wind speed) and 12,810 (air temperature) overlapping hours of AWS observations and ERA5 data (May 2019 to December 2020). We evaluate the performance using a fivefold cross-validation, with results indicating very strong agreement between the observed and downscaled meteorology: hourly Pearson correlations range from 0.83 (relative humidity) to 0.98 (air temperature), translating respectively to root mean square errors between ~31 and 8% of the observed means (Supplementary Fig. 7). We also detect no sign of a seasonal dependence in the performance of bias correction and RF models (Supplementary Fig. 8). The resulting downscaled ERA5 data provide a complete annual series of hourly values for 1950–2019.We estimate precipitation at the South Col also using ERA5. First, we linearly interpolate the reanalysis data to the location of the Phortse AWS5 and then compute the ratio of the total observed precipitation (Po) and ERA5 precipitation (PE) during the overlapping period (April 2019-November 2020). We then multiply all reanalysis precipitation by this scalar to produce a corrected precipitation series (PE’) for Phortse 1950–2019:$$P_{{{mathrm{E}}}}^prime = frac{{P_{{{mathrm{o}}}}}}{{P_{{{mathrm{E}}}}}}P_{{{mathrm{E}}}}$$
(5)
To extrapolate to the South Col, we assume that precipitation decays exponentially with increasing elevation30. However, we recalibrate the regression using the Phortse and Basecamp AWSs because these new sites have weighing precipitation gauges protected by double alter shields5, and hence are less prone to under-catch error (Supplementary Fig. 9). Note that, as described below, the precipitation estimate is adjusted to an “effective” flux before being used to simulate glacier mass balance changes.Mass balanceWe use the precipitation, along with the other downscaled meteorological variables, to force the COSIPY model20 at hourly resolution for 1950–2019. First, we compute the effective precipitation (which implicitly includes the net effects of avalanching and wind transport, as well as correcting for any systematic bias in the downscaling/extrapolation method described above) required for the glacier to be in equilibrium for the period 1950–1959, iterating until the surface mass balance is zero. This is achieved when the precipitation is decreased by 65%. We then run two simulations with COSIPY. The first (referred to as the “snow” simulation) assumes a starting snowpack that is arbitrarily deep (20 m) to ensure that it remains present throughout the entire (70-year) simulation. We set the initial surface density of the snowpack to 350 kg m−3, the bottom density to 800 kg m−3, and linearly interpolate between. The second simulation (hereafter the “ice” simulation) uses the same effective precipitation but assumes no initial snowpack. However, snow is free to accumulate in the model in response to meteorological forcing. The algorithms and parameter values used in our application of COSIPY are outlined in Methods Table 2.Table 2 Parameterizations and parameter values used in the COSIPY model runs.Full size tableMeltThe surface melt rate depends on the surface energy balance (SEB):$$Q_{{{mathrm{h}}}} + Q_{{{mathrm{l}}}} + Q_{{{{mathrm{lw}}}}} + Q_{{{{mathrm{sw}}}}} + Q_{{{mathrm{g}}}} + Q_{{{mathrm{r}}}} – Q_{{{mathrm{m}}}} = 0$$
(6)
where Q denotes energy flux (W m−2) and the subscripts h, l, lw, sw, g, and r refer to the sensible, latent, net longwave radiative, net shortwave radiative, ground, and precipitation heat fluxes, respectively. The fluxes are defined as positive when directed towards the surface. The energy consumed in melting (Qm) is also defined as positive, meaning the melt rate (M; mm w.e. s−1 or kg s−1) can be calculated:$$M = frac{1}{{L_{{{mathrm{f}}}}}}mathop {sum}Hleft( {Q_{{{mathrm{m}}}},T_{{{mathrm{s}}}}} right)Q_i$$
(7)
in which H(Q,Ts) is a Heaviside function that returns a value of one unless both the sum of the first six terms in Eq. (6) (Qm = ∑Qi, with i indexing terms Qh to Qr) is positive and the surface temperature is also at the melting point; otherwise, it returns zero. The melt total over a period of ∆t seconds can then be expressed:$$M = Pfrac{{{Delta}t}}{{L_{{{mathrm{f}}}}}}{sum} {overline {Q_i} }$$
(8)
where P is the fraction of ∆t during which melting conditions occurred, and the overbar for energy component Qi indicates the mean value calculated during melting conditions. In terms of energy components Eq. (8) is the major driver of the amplification in melt totals between the snow and ice COSIPY simulations, increasing by a factor of 4.4; the energy sinks (sum of the remaining terms) amplify by a factor of 3.6 (Methods Fig. 3). The resulting amplification in mean melt rate (left( {acute{A} = frac{{left( {{sum} {overline {Q_i} } } right)_{{{{mathrm{ice}}}}}}}{{left( {{sum} {overline {Q_i} } } right)_{{{{mathrm{snow}}}}}}}} right)), though, is by almost a factor of 500. To understand this result, note that the proportional increase in melt rate can be written:$$acute{A} = frac{{koverline {Q_{{{{mathrm{sw}}}}}} – joverline {Q_{{{{mathrm{sinks}}}}}} }}{{overline {Q_{{{{mathrm{sw}}}}}} – overline {Q_{{{{mathrm{sinks}}}}}} }}$$
(9)
where (overline {Q_{{{{mathrm{sw}}}}}}) and (overline {Q_{{{{mathrm{sinks}}}}}}) are the mean energy gains and losses, respectively, during melt conditions in the snow simulation, and k and j are the proportional increases in these terms when transitioning to an ice surface (4.3 and 3.6, respectively). Critically, Eq. (9) reveals that (acute{A}) is inversely proportional to the baseline melt rate in the snow simulation (left( {overline {Q_{{{{mathrm{sw}}}}}} – overline {Q_{{{{mathrm{sinks}}}}}} } right)). The very low melt rate in the snow scenario (3.3 mm w.e. a−1), therefore acts to amplify the numerator of in Eq. (9).Sublimation and melt sensitivity to climate forcingTotal sublimation (S, mm w.e. or kg) can be written:$$S = rho ;U;C_{{{mathrm{e}}}}(e_{{{mathrm{s}}}} – e_{{{mathrm{a}}}}{Upsilon})frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(10)
where ρ is the air density (kg m−3), U is the wind speed (m s−1), Ce is the turbulent exchange coefficient for moisture (dimensionless), ε is the ratio of gas constants for water vapor and air (0.622), Pa is air pressure (Pa), and Υ is relative humidity (fraction). The saturation vapor pressure for the surface (es) and the near-surface atmosphere (ea) are functions of the surface (Ts) and air temperature (Ta), respectively. If we assume that Ts = Ta (which is a reasonable simplification at the South Col where air temperature does not rise above 0 °C), and use the Clausius Clapeyron equation:$$e_{{{mathrm{s}}}} = e_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}$$
(11)
in which e0 is the saturation vapor pressure at the melting point, L is the latent heat of sublimation (2.83 × 106 J kg−1), and Rv is the gas constant for moist air (461 J K−1); Eq. (10) becomes:$$S = rho ;U;C_{{{mathrm{e}}}}(1 – {Upsilon})e_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(12)
which can be differentiated with respect to U, Ta, and Υ to explore the sensitivity of sublimation to changes in these meteorological parameters. In turn, the contribution of temporal trends (left( {frac{{{mathrm{d}}x}}{{{mathrm{d}}t}}} right)) in these variables to the trend sublimation can be evaluated via the chain rule:$$frac{{{mathrm{d}}S}}{{{mathrm{d}}t}} = frac{{partial S}}{{partial U}}frac{{{mathrm{d}}U}}{{{mathrm{d}}t}} + frac{{partial S}}{{partial Y}}frac{{{mathrm{d}}Y}}{{{mathrm{d}}t}} + frac{{partial S}}{{partial T_{{{mathrm{a}}}}}}frac{{{mathrm{d}}T_{{{mathrm{a}}}}}}{{{mathrm{d}}t}}$$
(13)
With:$$frac{{partial S}}{{partial U}} = rho ;C_{{{mathrm{e}}}};e_0left( {1 – {Upsilon}} right)e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(14)
$$frac{{partial S}}{{partial T_{{{mathrm{a}}}}}} = L;C_{{{mathrm{e}}}};e_0;rho ;Uleft( {1 – {Upsilon}} right)frac{{e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}}}{{R_{{{mathrm{v}}}}P_{{{mathrm{a}}}}T_{{{mathrm{a}}}}^2}}varepsilon {Delta}t$$
(15)
$$frac{{partial S}}{{partial {Upsilon}}} = – rho ;C_{{{mathrm{e}}}};e_0;Ue^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(16)
To evaluate Eq. (13) we compute the derivatives (Eqs. (14)–(16)) using the mean meteorology at the South Col during the ERA5 reconstruction (1950–2019), and ∆t to the number of seconds in 1 year (3.2 × 107 s). We prescribe the turbulent exchange coefficient (Ce) using the output from the COSIPY snow simulation, dividing the simulated sublimation by (rho ;U(e_{{{mathrm{s}}}} – e_{{{mathrm{a}}}}Upsilon )frac{varepsilon }{{P_{{{mathrm{a}}}}}}Delta t) (see Eq. (12)).Inserting these values into Eqs. (14) (16) yields:$$frac{{partial S}}{{dU}} = 6.0;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}};{{{mathrm{a}}}}^{ – 1}{{{mathrm{m}}}}^{ – 1};{{{mathrm{s}}}}^1$$$$frac{{partial S}}{{d{Upsilon}}} = – 1.8;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}};{{{mathrm{a}}}}^{ – 1}% ^{ – 1}$$$$frac{{partial S}}{{dT_{{{mathrm{a}}}}}} = 6.7,{{{mathrm{mm}}}},{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}},{{{mathrm{a}}}}^{ – 1},^circ {{{mathrm{C}}}}^{ – 1}$$We then estimate the time derivatives for Eq. (13) using the Theil-Sen slope estimation, yielding:$$frac{{{{d}}{U}}}{{{{d}}t}} = – 0.08;{mathrm{m}};{mathrm{s}}^{-1} ;{{{mathrm{decade}}}}^{ – 1};{{{mathrm{and}}}};frac{{partial S}}{{partial {U}}}frac{{{{d}}{U}}}{{{{d}}t}}; = ;0.05;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}.;{{{mathrm{a}}}}^{ – 2}$$$$frac{{{{d}}{Upsilon}}}{{{{d}}t}} = – 0.6% ;{{{mathrm{decade}}}}^{ – 1};{{{mathrm{and}}}};frac{{partial S}}{{partial {Upsilon}}}frac{{{{d}}{Upsilon}}}{{{{d}}t}}; = ;0.11;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}.;{{{mathrm{a}}}}^{ – 2}$$$$frac{{{{d}}T_a}}{{{{d}}t}} = 0.16;^circ {{{mathrm{C}}}};{{{mathrm{decade}}}}^{ – 1}{{{mathrm{and}}}};frac{{partial S}}{{partial T_{{{mathrm{a}}}}}}frac{{{{d}}T_{{{mathrm{a}}}}}}{{{{d}}t}}; = ;0.11;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}};{{{mathrm{a}}}}^{ – 2}$$Summing these terms indicates a theoretical trend (left( {frac{{{mathrm{d}}S}}{{{mathrm{d}}t}}} right)) of 0.27 mm w.e. a−2, in reasonably close agreement with the 0.22 mm w.e. a−2 derived from the COSIPY snow simulation and reported in the main text. This theoretical analysis also indicates that 82% of the trend can be attributed to increasing air temperature (41%) and declining relative humidity (41%), with strengthening winds explaining the remaining 18%.An alternative (empirical) method to estimate the sensitivity of sublimation in the COSIPY snow simulation is to use multiple linear regression:$$S = alpha + beta _{{{mathrm{U}}}}U + beta _{{{mathrm{{Upsilon}}}}}{Upsilon} + beta _{T_{{{mathrm{a}}}}}T_{{{mathrm{a}}}}$$
(17)
Where the slope coefficients (βx) are linear approximations of the derivatives, relating the changes in the annual mean of the meteorological variables to the total annual sublimation. Performing the regression (Supplementary Fig. 11) lends support to the interpretation from the theoretical analysis above, attributing 49, 26, and 25% of the sublimation increase to the trends in air temperature relative humidity, and wind speed, respectively.In the main text, we highlight that sublimation and melt rates may differ in their response to climate forcing. To support this assertion, we repeat the sensitivity assessment above, evaluating the derivatives of the melt rate with respect to air temperature, wind speed, and relative humidity.We simplify the analysis by assuming that the proportion of time that the surface is melting (P) is constant (see Eq. (8)). Although physically incomplete, we note that there is no temporal trend in P for the COSIPY snow simulation (p  > 0.05 according to Seil-Then slope estimation). With this simplification, the sensitivity of the melt rate to changes in meteorological component x can then be written:$$frac{{{mathrm{d}}M}}{{{mathrm{d}}x}} = Pfrac{{{Delta}t}}{{L_{{{mathrm{f}}}}}}{sum} {frac{{partial underline {Q_i} }}{{partial x}}}$$
(18)
Wind speed, air temperature, and relative humidity appear in the expressions for the sensible, latent, and longwave heat fluxes (Qh, Ql, and Qlw, respectively):$$Q_h = rho ;U;c_{{{mathrm{p}}}};C_{{{mathrm{h}}}}(T_{{{mathrm{a}}}} – 273.15)$$
(19)
$$Q_l = rho ;U;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}}({Upsilon}e_{{{mathrm{a}}}} – 611.3)frac{varepsilon }{{P_{{{mathrm{a}}}}}}$$
(20)
$$Q_{lw} = alpha sigma T_{{{mathrm{a}}}}^4 – 312.5$$
(21)
In which we have assumed melting conditions (Ts = 273.15, e0 = 611.3 Pa; Lv is the latent heat of vaporization [2.5 × 105 J kg−1], and the longwave thermal radiation emitted by the snow surface is 312.5 W m−2); cp is the specific heat content of the air (1004.7 J kg−1 K−1). It has been concluded31 that the incident longwave flux Qlw↓ in the Himalaya may be estimated from Υ and Ta:$$Q_{{{{mathrm{lw}}}}} downarrow = c_1 + c_2{Upsilon} + c_3sigma T_{{{mathrm{a}}}}^4 – 312$$
(22)
where the cx terms are empirically determined coefficients, whose value depends on cloudiness. Optimizing this expression for the South Col AWS, we found c1 = −17 (−168) W m−2, c2 = 0.73 (2.12) W m−2 %−1 and c3 = 0.57 (0.84) (dimensionless) for clear (cloudy) conditions.The derivative of these fluxes with respect to air temperature is then:$$frac{{partial Q_{{{mathrm{h}}}}}}{{partial T_{{{mathrm{a}}}}}} = rho ;U;c_{{{mathrm{p}}}};C_{{{mathrm{h}}}}$$
(23)
$$frac{{partial Q_{{{mathrm{l}}}}}}{{partial T_{{{mathrm{a}}}}}} = rho ;U;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}};L;{Upsilon};varepsilon ;e_0frac{{e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}}}{{R_{{{mathrm{v}}}}P_{{{mathrm{a}}}}T_{{{mathrm{a}}}}^2}}$$
(24)
$$frac{{partial Q_{{{{mathrm{lw}}}}}}}{{partial T_{{{mathrm{a}}}}}} = 4sigma c_3T_{{{mathrm{a}}}}^3$$
(25)
Note that all terms in Eqs. (23)–(25) are positive, outlining the physical basis for why melt rates should increase with rising air temperature32.The derivative of these fluxes with respect to relative humidity is:$$frac{{partial Q_{{{mathrm{l}}}}}}{{partial {Upsilon}}} = rho ;U;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}};e_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}$$
(26)
$$frac{{partial Q_{{{{mathrm{lw}}}}}}}{{partial {Upsilon}}} = c_2$$
(27)
Because all terms are positive in Eqs. (26) and (27), increases in relative humidity also drive increases in the melt rate.The derivatives of the sensible and latent heat fluxes with respect to wind speed are then:$$frac{{partial Q_{{{mathrm{h}}}}}}{{partial U}} = rho ;c_{{{mathrm{p}}}};C_{{{mathrm{h}}}}(T_{{{mathrm{a}}}} – 273.15)$$
(28)
$$frac{{partial Q_{{{mathrm{l}}}}}}{{partial U}} = rho ;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}}frac{varepsilon }{{P_{{{mathrm{a}}}}}}(Ye_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)} – 611.3)$$
(29)
Because Ta is always less than 273.15 K during melt events at the South Col in the COSIPY snow simulation (Supplementary Fig. 12), (e_0;e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}) must be less than 611.3 Pa. Hence, the last terms in Eqs. (28) and (29) are negative, and so increases in wind speed act to reduce the melt rate.In summary, then, theory indicates that rising air temperatures should accelerate both sublimation and melt rates (Eqs. (15) and (24)). However, increases in wind speed and relative humidity will have opposite effects. Due to the persistence of freezing air temperatures during surface melt events, faster winds act to enhance sublimation (Eq. (14)) but reduce melting (Eqs. (28) and (29)), whereas increasing relative humidity amplifies melting (Eqs. (26) and (27)) but dampens sublimation (Eq. (16)). More

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