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Ongoing runoff declines in the Colorado River Basin have been shown to be predominately driven by decreasing albedo from warming‐driven snow‐cover loss, especially in late‐spring (hereafter snowmelt‐radiation feedback). Here, we explore the feedback's impact on annual runoff sensitivity to warming across the western U.S. (WUS) using hydrologic model simulations. For 1°C uniform warming, we show that runoff is most sensitive to warming in modestly snow‐covered, interior mountain headwaters, especially the Rocky Mountains. Runoff sensitivities are most associated with the snowmelt‐radiation feedback in basins with runoff coefficients between 0.2 and 0.6, where runoff sensitivity increases with more snow and lower winter temperature. In aggregate, ∼48% of WUS runoff sensitivity is attributable to the snowmelt‐radiation feedback and is especially pronounced in the warming‐sensitive river basins (annual runoff decreases >5%/°C). We also show that the feedback's impact decreases with increasing temperature, which has unresolved implications for streamflow declines in a less‐snow future. Plain Language Summary Regional climate warming is driving strong runoff changes in the western U.S. (WUS), especially the Upper Colorado River Basin (UCRB). Previous work showed that warming‐related snow cover reductions lead to more solar radiation absorption and evapotranspiration, which largely explain ongoing runoff declines in UCRB. Here, we assess the impact of this snowmelt‐radiation feedback on warming‐induced runoff changes across WUS. In a warmer world, we find that the largest annual runoff sensitivities are in the interior mountainous WUS with modest snow cover. The snowmelt‐radiation feedback explains over half of the warming‐induced runoff changes in warming‐sensitive WUS basins and about half of WUS' overall runoff sensitivity. In areas influenced by the snowmelt‐radiation feedback, both runoff sensitivity and the feedback's contribution become smaller with higher temperatures, suggesting a potentially slower rate of streamflow decline as temperatures rise in a warmer future. Key Points Snowmelt‐radiation feedback accounts for ∼1/2 of warming‐driven runoff decline across the Western U.S. (WUS) Runoff sensitivities are most linked to snowmelt‐radiation feedback in river basins with runoff coefficients in the range 0.2–0.6 Runoff sensitivities to warming are largest in modestly snow‐covered, interior mountainous parts of WUS, especially the Rocky Mountains

Understanding the sources of light-absorbing organic (brown) carbon (BrC) and its interaction with black carbon (BC) and other non-refractory particulate matter (NR-PM) fractions is important for reducing uncertainties in the aerosol direct radiative forcing. In this study, we combine multiple filter-based techniques to achieve long-term, spectrally resolved, source- and species-specific atmospheric absorption closure. We determine the mass absorption efficiency (MAE) in dilute bulk solutions at 370 nm to be equal to 1.4 m² g⁻¹ for fresh biomass smoke, 0.7 m² g⁻¹ for winter-oxygenated organic aerosol (OA), and 0.13 m² g⁻¹ for other less absorbing OA. We apply Mie calculations to estimate the contributions of these fractions to total aerosol absorption. While enhanced absorption in the near-UV has been traditionally attributed to primary biomass smoke, here we show that anthropogenic oxygenated OA may be equally important for BrC absorption during winter, especially at an urban background site. We demonstrate that insoluble tar balls are negligible in residential biomass burning atmospheric samples of this study and thus could attribute the totality of the NR-PM absorption at shorter wavelengths to methanol-extractable BrC. As for BC, we show that the mass absorption cross-section (MAC) of this fraction is independent of its source, while we observe evidence for a filter-based lensing effect associated with the presence of NR-PM components. We find that bare BC has a MAC of 6.3 m² g⁻¹ at 660 nm and an absorption Ångström exponent of 0.93 ± 0.16, while in the presence of coatings its absorption is enhanced by a factor of ∼ 1.4. Based on Mie calculations of closure between observed and predicted total light absorption, we provide an indication for a suppression of the filter-based lensing effect by BrC. The total absorption reduction remains modest, ∼ 10 %–20 % at 370 nm, and is restricted to shorter wavelengths, where BrC absorption is significant. Overall, our results allow an assessment of the relative importance of the different aerosol fractions to the total absorption for aerosols from a wide range of sources and atmospheric ages. When integrated with the solar spectrum at 300–900 nm, bare BC is found to contribute around two-thirds of the solar radiation absorption by total carbonaceous aerosols, amplified by the filter-based lensing effect (with an interquartile range, IQR, of 8 %–27 %), while the IQR of the contributions by particulate BrC is 6 %–13 % (13 %–20 % at the rural site during winter). Future studies that will directly benefit from these results include (a) optical modelling aiming at understanding the absorption profiles of a complex aerosol composed of BrC, BC and lensing-inducing coatings; (b) source apportionment aiming at understanding the sources of BC and BrC from the aerosol absorption profiles; (c) global modelling aiming at quantifying the most important aerosol absorbers.

Melting of the Greenland Ice Sheet (GrIS) is the largest single contributor to eustatic sea level and is amplified by the growth of pigmented algae on the ice surface, which increases solar radiation absorption. This biological albedo-reducing effect and its impact upon sea level rise has not previously been quantified. Here, we combine field spectroscopy with a radiative-transfer model, supervised classification of unmanned aerial vehicle (UAV) and satellite remote-sensing data, and runoff modelling to calculate biologically driven ice surface ablation. We demonstrate that algal growth led to an additional 4.4–6.0 Gt of runoff from bare ice in the south-western sector of the GrIS in summer 2017, representing 10 %–13 % of the total. In localized patches with high biomass accumulation, algae accelerated melting by up to 26.15±3.77 % (standard error, SE). The year 2017 was a high-albedo year, so we also extended our analysis to the particularly low-albedo 2016 melt season. The runoff from the south-western bare-ice zone attributed to algae was much higher in 2016 at 8.8–12.2 Gt, although the proportion of the total runoff contributed by algae was similar at 9 %–13 %. Across a 10 000 km2 area around our field site, algae covered similar proportions of the exposed bare ice zone in both years (57.99 % in 2016 and 58.89 % in 2017), but more of the algal ice was classed as “high biomass” in 2016 (8.35 %) than 2017 (2.54 %). This interannual comparison demonstrates a positive feedback where more widespread, higher-biomass algal blooms are expected to form in high-melt years where the winter snowpack retreats further and earlier, providing a larger area for bloom development and also enhancing the provision of nutrients and liquid water liberated from melting ice. Our analysis confirms the importance of this biological albedo feedback and that its omission from predictive models leads to the systematic underestimation of Greenland's future sea level contribution, especially because both the bare-ice zones available for algal colonization and the length of the biological growth season are set to expand in the future.

A set of dedicated radiative kernels are generated to quantify the individual contributions of sea ice and clouds on the interannual variation of absorbed solar radiation (ASR) observed over the Arctic ocean. From spring to early summer, changes in ASR are associated more with sea ice than with the clouds. Conversely, the cloud contribution explains more of the ASR anomaly after June. Overall, variations in cloud cover and sea ice extent can explain 50%–93% of the ASR variability in each sunlit month. Both the sea‐ice‐ and cloud‐associated ASR variations are positively correlated with the observed ASR anomaly, but their trends over the 20‐year observational period are opposite. The positive ASR trend revealed by the kernel decomposition, primarily driven by the observed decline in sea ice extent, is dampened by clouds, particularly in June. Annually, clouds counteract 55% of the ASR trend induced by Arctic sea ice loss.

Reanalysis data and numerical model are employed to uncover the mechanisms of spring (March–April) Arctic surface warming. Different from other seasons, little additional solar radiation absorption or seasonal heat storage release contributes to Arctic surface warming in spring. Both observation and numerical results suggest that horizontal heat advection dominates Arctic surface air warming. However, horizontal advection originates from lower latitudes instead of local energy redistribution as in other seasons. Furthermore, Arctic warming weakens the meridional potential vorticity gradient, which strengthens the synoptic atmospheric blocking event. As a result, more warm (cold) air is transported to higher (lower) latitudes along the edge of high pressure, which is an accumulation of atmospheric blocking events. The results suggest that anomalous meridional heat transport plays more important roles in Arctic surface warming when the anomalous radiative forcing is weak. Without being absorbed by the ocean, additional available energy induces strong Arctic springtime surface warming. Plain Language Summary Arctic spring warming does not occur due to local forcing and feedback, which means surface albedo feedback (solar radiation anomaly) and seasonal heat storage do not dominate Arctic surface warming in spring. Synoptic activities, such as atmospheric blocking or eddies, are highly influenced by climate change. Alternatively, atmospheric blocking or eddy anomalies influence the climate, that is, enhance horizontal temperature advection, which becomes the dominant warming mechanism in the spring Arctic. In this study, we focus on remote forcings. Key Points Surface albedo feedback and seasonal heat storage have much smaller influences on Arctic spring warming than other seasons Late winter Arctic warming triggers a weaker meridional potential vorticity gradient in spring, resulting in atmospheric blocking with greater strength Longer‐existing and stronger atmospheric blocking events induce anticyclonic wind, transporting additional energy meridionally in spring

The Southern Ocean, characterized by strong westerly winds and a rough sea state, exhibits the most pronounced sea spray effects. Sea spray ejected by ocean surface waves enhances heat and water exchange at the air—sea interface. However, this process has not been considered in current climate models, and the influence of sea spray on the coupled air—sea system remains largely unknown. This study incorporated a parameterization of the sea spray influence on latent and sensible heat fluxes into the First Institute of Oceanography Earth System Model version 2.0 (FIO-ESM v2.0), a climate model coupled with an ocean surface waves component. The results indicate that the spray-mediated enthalpy flux accounted for over 20%–50% of the total value. Sea spray promoted ocean evaporation and heat transport, resulting in air and ocean surface cooling and strengthened westerly winds. Furthermore, a moist and stable atmosphere favored an increase in cloud fraction over the Southern Ocean, particularly low-level clouds. Increased clouds reflected downward shortwave radiation and reduced solar radiation absorption at the surface. At present, the climate models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer notable deficiencies in reasonably reproducing the climatological features of the Southern Ocean, including warm SST and underestimated clouds biases with more absorbed shortwave radiation. Our results suggest that consideration of sea spray effects is a feasible solution to mitigate these common biases and enhance the confidence in simulations and predictions with climate models.

Black carbon (BC), brown carbon (BrC), and soil dust are the most important radiation-absorbing aerosols (RAAs). When RAAs are deposited on the snowpack, they lower the snow albedo, causing an increase in the solar radiation absorption. The climatic impact associated with the snow darkening induced by RAAs is highly uncertain. The Intergovernmental Panel on Climate Change (IPCC) Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) attributes low and medium confidence to radiative forcing (RF) from BrC and dust in snow, respectively. Therefore, the contribution of anthropogenic sources and carbonaceous aerosols to RAA RF in snow is not clear. Moreover, the snow albedo perturbation induced by a single RAA species depends on the presence of other light-absorbing impurities contained in the snowpack. In this work, we calculated the present-day RF of RAAs in snow starting from the deposition fields from a 5-year simulation with the GEOS-Chem global chemistry and transport model. RF was estimated taking into account the presence of BC, BrC, and mineral soil dust in snow, simultaneously. Modeled BC and black carbon equivalent (BCE) mixing ratios in snow and the fraction of light absorption due to non-BC compounds (fnon-BC) were compared with worldwide observations. We showed that BC, BCE, and fnon-BC, obtained from deposition and precipitation fluxes, reproduce the regional variability and order of magnitude of the observations. Global-average all-sky total RAA-, BC-, BrC-, and dust-snow RF were 0.068, 0.033, 0.0066, and 0.012 W m−2, respectively. At a global scale, non-BC compounds accounted for 40 % of RAA-snow RF, while anthropogenic RAAs contributed to the forcing for 56 %. With regard to non-BC compounds, the largest impact of BrC has been found during summer in the Arctic (+0.13 W m−2). In the middle latitudes of Asia, the forcing from dust in spring accounted for 50 % (+0.24 W m−2) of the total RAA RF. Uncertainties in absorbing optical properties, RAA mixing ratio in snow, snow grain dimension, and snow cover fraction resulted in an overall uncertainty of −50 %/+61 %, −57 %/+183 %, −63 %/+112 %, and −49 %/+77 % in BC-, BrC-, dust-, and total RAA-snow RF, respectively. Uncertainty upper bounds of BrC and dust were about 2 and 3 times larger than the upper bounds associated with BC. Higher BrC and dust uncertainties were mainly due to the presence of multiple absorbing impurities in the snow. Our results highlight that an improvement of the representation of RAAs in snow is desirable, given the potential high efficacy of this forcing.

The purpose of this study is to understand a physical mechanism to determine the surface temperature of clothes in calm and fine conditions of outdoors. We observed surface temperatures of polo shirts of the same material and design but different colors. The shirts were placed in unshaded and well-ventilated outdoor, open spaces on sunny summer days. The maximum difference between dark green or black and white was more than 15 °C during calm, fine weather and was greatest when the solar radiation was strong. If the transmission of solar radiation energy through a shirt is ignored to calculate the absorption by the shirt, the difference in solar radiation absorption due to different colors is as much as 24% in the maximum, and if considered, we concluded that an absorption difference of 34% led to a temperature difference of 15℃. When we compared the brightness of the colors, we found that the albedo of both the visible and NIR bands explained why the red and green colors were so different with respect to the surface temperatures we observed. The reflection in the NIR bands was also an important determinant of the surface temperature. An additional experiment using masks showed that the temperature difference between white and black was almost eliminated at a wind speed of ~ 3 m/s. The color of clothing is therefore a target for small-scale adaptation to climate change.

Most climate models show systematic cold biases during snow‐covered period over the Tibetan Plateau (TP), which is associated with snow and surface albedo overestimations. In this work, a snow cover fraction (SCF) scheme and a recently developed albedo scheme for shallow snow are implemented in the Noah‐MP land surface model coupled with the Weather Research and Forecasting (WRF) model. The SCF scheme introduces subgrid orographic variability to reduce the SCF, and the shallow‐snow albedo scheme parameterizes the fresh‐snow albedo as a function of the snow depth (SD). Evaluations by remote sensing data show that both schemes can effectively alleviate the overestimation of the simulated surface albedo, SCF, snow water equivalent, and SD over the TP. The reductions in the modeled SCF and snow albedo directly lead to lower surface albedo values and thus more surface solar radiation absorption, which accelerates snow melting and causes surface warming effects. Further comparisons with Moderate Resolution Imaging Spectroradiometer data and station observations show that both schemes can significantly reduce the cold biases in the surface skin temperature (from −4.39°C to 0.19°C for the TP mean) and 2‐m air temperature (from −4.48°C to −1.05°C for the station mean) during the cold season (October to May of next year) in the study region. This work provides guidance for advancing the snow‐related physics in climate models and the improved WRF model could facilitate weather forecasting and climate prediction for the plateau region. The cold bias of the Weather Research and Forecasting model over the Tibetan Plateau is significantly reduced by implementing a snow coverage‐topography relationship and a fresh snow albedo scheme. With the introduction of the subgrid orographic variability in parameterizing the snow cover fraction and a shallow‐snow albedo scheme in parameterizing the fresh‐snow albedo, less snow and a lower surface albedo are simulated. Thus, more solar radiation is absorbed by the land surface, leading to a surface warming effect. As a result, the cold biases in the surface skin temperature and 2‐m air temperature are significantly reduced when evaluated by Moderate Resolution Imaging Spectroradiometer data and station observations. A snow coverage‐topography relationship and a fresh snow albedo scheme are implemented in Weather Research and Forecasting and applied to the Tibetan Plateau (TP) The overestimation in the simulated snow cover, snow depth (SD) and albedo over the TP is significantly alleviated The modeled cold biases over the TP are significantly reduced due to the enhanced surface net solar radiation induced by albedo reduction

The timing of snowmelt onset (SMO) is a critical climate indicator in the Arctic. However, spaceborne, in-situ measurements, and model simulations yield different estimates for the timing. Understanding these discrepancies is essential for identifying the physical mechanisms driving SMO. In this study, SMO, snow, and sea ice thermodynamics were simulated using a single-column snow/ice model (HIGHTSI) along trajectories of 42 ice mass balance buoys operating in the period of 2010 to 2015. The results were compared with passive microwave remote sensing and ice mass balance observations. The modeled surface-SMO has a high inter-annual correlation (0.94) with the ice mass balance-derived results but occurred on average 5 days earlier than observations. The remote-sensing-derived Early-SMO was 12 days before the ice mass balance-derived surface-SMO, while the Continuous-SMO showed a 5 day lag. The modeled average snow depth, ice thickness, and snow/ice temperature captured the recorded seasonal variations. The modeled snow/ice temperature showed seasonal biases of 0.4°C/0.5°C between May–September, and −2.7°C/−4.6°C between October–April, respectively. The corresponding biases for average snow depth and ice thickness were −0.05 m/−0.15 m and 0.03 m/0.14 m, respectively. Accurate representation of air temperature forcing and solar radiation absorption is crucial for realistic simulation of SMO.