Changes In Surface Albedo Research Articles

Reponses of Land Surface Albedo to Global Vegetation Greening: An Analysis Using GLASS Data

Global vegetation greening during recent decades has been observed from various remote sensing data. The global and regional climate can be altered by an increase in carbon storage, as well as changes in land surface albedo (LSA) and evaporation. However, the LSA changes induced by global vegetation greening are still not clear, and contrasting responses of LSA to vegetation changes were reported in previous studies. In this study, we analyzed the LSA in response to global vegetation greening using the Global Land Surface Satellite (GLASS) data and a vegetation-induced LSA change model. The results showed that vegetation greening trends could be observed worldwide, which resulted in contrasting LSA responses at regional scales (LSA increased as leaf area index (LAI) increased, or LSA decreased as LAI increased). Moreover, these contrasting LSA responses to global vegetation greening were effectively explained by the albedo difference between a vegetation and soil background. The results provide new insights into the relationship between LSA changes and global vegetation dynamics, and can support recommendations for policies of vegetation protection, and large-scale afforestation and deforestation.

Surface Albedo and Snowline Altitude Estimation Using Optical Satellite Imagery and In Situ Measurements in Muz Taw Glacier, Sawir Mountains

Glacier surface albedo strongly affects glacier mass balance by controlling the glacier surface energy budget. As an indicator of the equilibrium line altitude (ELA), the glacier snowline altitude (SLA) at the end of the melt season can reflect variations in the glacier mass balance. Therefore, it is extremely crucial to investigate the changes of glacier surface albedo and glacier SLA for calculating and evaluating glacier mass loss. In this study, from 2011 to 2021, the surface albedo of the Muz Taw Glacier was derived from Landsat images with a spatial resolution of 30 m and from the Moderate Resolution Imaging Spectroradiometer albedo products (MOD10A1) with a temporal resolution of 1 day, which was verified through the albedo measured by the Automatic Weather Station (AWS) installed in the glacier. Moreover, the glacier SLA was determined based on the variation in the surface albedo, with the altitude change along the glacier main flowline derived from the Landsat image at the end of the melt season. The correlation coefficient of >0.7, with a risk of error lower than 5%, between the surface albedo retrieved from remote sensing images and the in situ measurement data indicated that the method of deriving the glacier surface albedo by the remote sensing method was reliable. The annual average albedo showed a slight upward trend (0.24%) from 2011 to 2021. A unimodal seasonal variation in albedo was demonstrated, with the downward trend from January to August and the upward trend from August to December. The spatial distribution of the albedo was not entirely dependent on altitude due to the dramatic effects of the topography and glacier surface conditions. The average SLA was 3446 m a.s.l., with a variation of 160 m from 2011 to 2021. The correlation analysis between the glacier SLA and annual mean temperature/annual precipitation demonstrated that the variations of the average SLA on the Muz Taw Glacier was primarily affected by the air temperature. This study improved our understanding of the ablation process and mechanism of the Muz Taw Glacier.

Spatial Heterogeneity and Temporal Variation in Urban Surface Albedo Detected by High-Resolution Satellite Data

Albedo is one of the key parameters in the surface energy balance and it has been altered due to urban expansion, which has significant impacts on local and regional climate. Many previous studies have demonstrated that changes in the urban surface albedo are strongly related to the city’s heterogeneity and have significant spatial-temporal characteristics but fail to address the albedo of the urban surface as a unique variable in urban thermal environment research. This study selects Beijing as the experimental area for exploring the spatial-temporal characteristics of the urban surface albedo and the albedo’s uniqueness in environmental research on urban spaces. Our results show that the urban surface albedo at high spatial resolution can better represent the urban spatial heterogeneity, seasonal variation, building canyon, and pixel adjacency effects. Urban surface albedo is associated with building density and height, land surface temperature (LST), and fractional vegetation cover (FVC). Furthermore, albedo can reflect livability and environmental rating due to the variances of building materials and architectural formats in the urban development. Hence, we argue that the albedo of the urban surface can be considered as a unique variable for improving the acknowledgment of the urban environment and human livability with wider application in urban environmental research.

The impacts of urban anthropogenic heat and surface albedo change on boundary layer meteorology and air pollutants in the Beijing-Tianjin-Hebei region

In this study, present and future anthropogenic heat flux (AH) and surface albedo (SA) in Beijing are estimated and incorporated into the WRF-Chem/UCM model. A series of nested model simulations are conducted at 9 km and 27 km horizontal grid resolution with 30 layers in the vertical to explore the sensitivity of model results to urban surface characteristics during the study period from 23 February to 13 March 2014, and the impacts of anthropogenic heat and surface albedo changes in the future on urban meteorology and air pollutants in Beijing. Model validation demonstrates that the model with the up-to-date urban surface parameters apparently improves predictions for both meteorological variables and chemical species compared with model results with default urban parameters. The combined effect of SA increase and AH decrease leads to consistent decreases in T2 (air temperature at 2 m above ground), WS10 (wind speed at 10 m) and PBLH (planetary boundary layer height) by up to 1.2 °C, 0.4 m s−1 and 200 m (33%), respectively, in Beijing averaged over the study period. In response to the meteorological changes, near surface PM2.5 (particulate matter with aerodynamic equivalent diameter below 2.5 μm) concentration increases by up to 15.6 μg m−3 (8.6%), whereas O3 concentration decreases by 2.6 ppb (25.1%), respectively. In daytime, near surface cooling due to SA increase and AH decrease causes a strong downdraft over Beijing urban areas, a wind divergence near the surface and an upslope wind over the western mountain, resulting in aerosol transport from urban district to western mountain slope. At nighttime, surface cooling is weaker than that in daytime, leading to a weaker downdraft over Beijing and a slight downslope wind in the mountain. The combined changes in SA and AH lead to a decrease in T2 by 1.2 °C, a decrease of O3 concentration by approximately 20%, and increases in PM2.5 and its components by 10–17% in daytime averaged over the urban areas of Beijing during the study period, and the above changes are weaker during haze days than those in clean days. The changes in urban surface parameters in Beijing in the future may be benefit for mitigating heat wave and O3 pollution risks, but could increase fine aerosol level and human health risk, which require an optimal strategy for co-benefits of climate change mitigation and air pollution control in future city planning.

Numerical simulation of the surface flux of an alpine grassland in the source region of the Yellow River by the land surface model

The simulation performance of the land surface model Community Land Model 5.0 (CLM5.0) is tested by using the eddy correlation system and micrometeorological tower observational data at the Maqu observation field in 2016. The results show that the CLM5.0 model has a good simulation effect on the soil temperature and can well reflect the seasonal variation characteristics of soil temperature. In addition, the model can well simulate the seasonal variation in shallow soil moisture (5 cm and 10 cm). However, the deviations between the simulated and observed values are large. CLM5.0 cannot simulate the water holding capacity of shallow soil well, resulting in its underestimation. The simulation performance of the upward longwave radiation and net radiation is substantially better than that of the upward shortwave radiation due to the change in surface albedo caused by precipitation from March to July. CLM5.0 can also well reproduce the variation trend of the latent heat flux and 5 cm soil heat flux (heat exchange of soil at 5 cm). When the leaf area index (LAI) and Medlyn slope values are changed to 10%, 20%, and 50% of the default values, the simulation effects of soil moisture at depths of 5 cm and 10 cm are significantly improved, and the best simulation effects are achieved at 0.5 times the default values. This result indicates that lower LAI and Medlyn slope values are more suitable for simulating shallow soil moisture.

Changes in snow and ice surface albedo and its impact on snow and ice area in the Wind River Range, Wyoming, USA

ABSTRACT Glacial and snow melt in the Wind River Range, Wyoming feed headwaters for the Missouri and Colorado River Systems, covering much of the western United States. Identifying the factors that have the greatest impact on snow and ice melt is important to managing the water resources in this region. This study examined snow and ice albedo, summer temperature and precipitation, and winter precipitation as potential significant factors related to snow and ice area changes. The Mann–Kendall statistical test was used to analyze changes in surface albedo on snow and ice over time (1985 to 2016) as derived from Landsat imagery among 5 basins in the Wind River Range. As well, a stepwise regression analysis was utilized in determining the significance of albedo along with summer mean temperature, total summer precipitation, and total winter precipitation in predicting snow and ice area. Among all the variables, albedo was found to have the greatest significance in relation to snow and ice area change. The significance of albedo on snow and ice melting in the Wind River Range is likely due to a positive feedback effect coupled with possible effects from annual increases in particulates from forest fires and fossil fuel production.

Irrigation and warming drive the decreases in surface albedo over High Mountain Asia

Human and climate induced land surface changes resulting from irrigation, snow cover decreases, and greening impact the surface albedo over High Mountain Asia (HMA). Here we use a partial information decomposition approach and remote sensing data to quantify the effects of the changes in leaf area index, soil moisture, and snow cover on the surface albedo in HMA, home to over a billion people, from 2003 to 2020. The study establishes strong evidence of anthropogenic agricultural water use over irrigated lands (e.g., Ganges–Brahmaputra) which causes the highest surface albedo decreases (≤ 1%/year). Greening and decreased snow cover from warming also drive changes in visible and near-infrared surface albedo in different areas of HMA. The significant role of irrigation and greening in influencing albedo suggests the potential of a positive feedback cycle where albedo decreases lead to increased evaporative demand and increased stress on water resources.

A Study of the Change in Surface Parameters during the Last Four Decades in the MuUs Desert Based on Remote Sensing Data

As an important part of the Earth’s environmental system, sandy soils are particularly sensitive to changes in the climatic environment. As one of the four major desert regions in China, the MuUs desert has transformed from a desert to an oasis after more than half a century of ecological management. In this paper, we analyzed the spatial and temporal patterns of surface albedo, evapotranspiration, and fraction vegetation cover in the MuUs desert based on the Global Land Surface Satellite (GLASS) product with high spatial and temporal resolution and assessed the relationships between their variability and snow cover, air temperature, and precipitation. It is of great significance to understand the effect of desertification control and climate change after the conversion of land surface types in the MuUs region. The results show that the desertification control in the MuUs area has achieved remarkable results since 1982. The fraction vegetation coverage of the MuUs desert showed a significant increasing trend, with an interannual change rate of 1.32% each decade−1. The surface albedo of MuUs desert decreased significantly. Affected by vegetation and snow cover, it was lower in summer and higher in winter. The evapotranspiration showed a significant upward trend, higher in summer and lower in winter, which is significantly correlated with the changes in surface albedo, air temperature, and vegetation. In addition, the local-scale biophysical effects caused by vegetation change have influenced the climate of the MuUs region, manifested as the increase in precipitation and air temperature. In general, with the support of relevant policies and human construction projects, the overall ecological environment in the MuUs desert is developing in a good way.

Long-range transport of Asian dust to the Arctic: identification of transport pathways, evolution of aerosol optical properties, and impact assessment on surface albedo changes

Abstract. Airborne dust is one of the most important natural aerosols; it has various environmental impacts on air quality, ocean fertilization, and the global climate change. Asian dust, representing one of the major dust sources in the world, has been widely studied due to its long-range transport capability. However, its transport to the Arctic has been less investigated. In this study, two typical transport routes were identified based on the recorded dust events in China during 2011–2015. Accordingly, two specific Asian dust long-range transport events were selected and compared, i.e., one observed at Barrow, Alaska (traveled mostly over lands within 6–7 d), and the other one observed at Alert, Canada (traveled mostly over oceans within 7–8 d). The transport routes of the two dust events had been cross-validated by using air mass trajectory modeling, meteorology reanalysis data, ground-based aerosol columnar and profiling observations, and spaceborne remote sensing. It was found that different transport routes to the Arctic had divergent effects on the evolution of aerosol properties, revealing different mixing extents between dust, anthropogenic particles, smoke, and sea salts. Based on the Snow, Ice, and Aerosol Radiative (SNICAR) model, the albedo simulation indicated that dust and elemental carbon together reduced the surface albedo by 0.35 % to 2.63 % compared to the pure snow condition. This study implied that the dust long-range transport from China to the Arctic was ubiquitous and may be a potential contributor to the Arctic regional climate.

Observational Constraint on the Contribution of Surface Albedo Feedback to the Amplified Tibetan Plateau Surface Warming

AbstractSurface albedo feedback (SAF) is one of the main causes of amplified warming over the Tibetan Plateau (TP). Several recent studies have used the latest reanalysis datasets to evaluate the SAF induced warming, but without fully considering the fidelity of the surface albedo change and surface downward solar radiation in the reanalysis datasets, which directly affect the amplitude of SAF induced warming. This study finds that the state‐of‐the‐art reanalysis datasets (ERA‐Interim, ERA5, MERRA, MERRA‐2, JRA‐55 and CRA) and climate models that participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6) exhibit varying biases compared with observations in both surface albedo change and surface downward solar radiation over the TP. The state‐of‐the‐art reanalysis datasets present no obvious advantages over the lower resolution but less constrained CMIP6 multi‐model ensemble in representing SAF related processes over the TP. The surface albedo change drives most of the spread in SAF induced warming. The reanalysis datasets and CMIP6 climate models reveal a significant linear relationship between surface albedo change and its contribution to surface temperature change over the TP. Using the observation constrained linear relationship and satellite surface albedo products, the spread of warming contribution due to SAF in reanalysis datasets and climate models is greatly reduced, the estimated TP warming due to SAF is in the range of 0.26–0.50 K in winter and 0.27–0.77 K in spring over recent decades.

The Importance of Capturing Local Measurement-Driven Adjustment of Modelled j(NO2)

Accurate photolysis rate constants are essential for simulation of local air quality but their values can vary substantially with changes in local meteorological and surface conditions. This study demonstrates the use of local radiometer measurements for capturing via hourly measurement-driven adjustment factors (MDAF) the temporal resolution needed to adjust clear-sky or cloud-free model estimates of j(NO2). Measurements simultaneously at two sites in the UK (Auchencorth Moss and Manchester) showed that TUV (v5.3) model estimates of j(NO2)↓ in cloud-free conditions (used as an example of modelled j-values) were, on average, approximately 45% larger than measured j(NO2)↓, which would lead to substantial model bias in the absence of local adjustment. At Auchencorth Moss, MDAF values based on 4π and 2π radiometer inlets generally agreed very well with each other (<6% average difference). However, under conditions of particularly high surface albedo (such as snow cover), increased upwelling local diffuse radiation yielded an MDAF derived using total radiation (sum of ↓ and ↑ components) ~40% larger than the MDAF derived using only ↓ radiation. The study has demonstrated: (1) the magnitude of potential impact of local conditions—principally cloud cover, but also changes in surface albedo—on assumed j-values; (2) that whilst annual mean MDAF values are similar at Auchencorth Moss and Manchester, there is no contemporaneous correlation between them at hourly resolution; hence MDAF values derived at one site cannot readily be applied at another site. These data illustrate the need to routinely deploy long-term radiometer measurements alongside compositional measurements to support atmospheric chemistry modelling.

The Midlatitude Response to Polar Sea Ice Loss: Idealized Slab-Ocean Aquaplanet Experiments with Thermodynamic Sea Ice

Abstract Slab-ocean aquaplanet simulations with thermodynamic sea ice are used to study the zonally symmetric mechanisms whereby polar sea ice loss impacts the midlatitude atmosphere. Imposed sea ice loss (difference without and with sea ice with historical CO2 concentration) leads to global warming, polar amplified warming, and a weakening of poleward atmospheric energy transport and the midlatitude storm-track intensity. The simulations confirm an energetic mechanism that predicts a weakening of storm-track intensity in response to sea ice loss, given the change of surface albedo and assuming a passive ocean. Namely, sea ice loss increases the absorption of shortwave radiation by the surface (following the decrease of surface albedo), which increases surface turbulent fluxes into the atmosphere thereby weakening poleward atmospheric energy transport. The storm-track intensity weakens because it dominates poleward energy transport. The quantitative prediction underlying the mechanism captures the weakening but underestimates its amplitude. The weakening is also consistent with weaker mean available potential energy (polar amplified warming) and scales with sea ice extent, which is controlled by the slab-ocean depth. The energetic mechanism also operates in response to sea ice loss due to melting (difference of the response to quadrupled CO2 with and without sea ice). Finally, the midlatitude response to sea ice loss in the aquaplanet agrees qualitatively with the response in more complex climate models. Namely, the storm-track intensity weakens and the energetic mechanism operates, but the method used to impose sea ice loss in coupled models impacts the surface response.

Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy

Surface albedo plays a controlling role in the surface energy budget, and albedo-induced radiative forcing has a significant impact on climate and environmental change (e.g., global warming, snow and ice melt, soil and vegetation degradation, and urban heat islands (UHIs)). Several existing review papers have summarized the algorithms and products of surface albedo as well as climate feedback at certain surfaces, while an overall understanding of various land types remains insufficient, especially with increasing studies on albedo management methods regarding mitigating global warming in recent years. In this paper, we present a comprehensive literature review on the variance pattern of surface albedo, the subsequent climate impact, and albedo management strategies. The results show that using the more specific term “surface albedo” is recommended instead of “albedo” to avoid confusion with similar terms (e.g., planetary albedo), and spatiotemporal changes in surface albedo can indicate subtle changes in the energy budget, land cover, and even the specific surface structure. In addition, the close relationships between surface albedo change and climate feedback emphasize the important role of albedo in climate simulation and forecasting, and many albedo management strategies (e.g., the use of retroreflective materials (RRMs)) have been demonstrated to be effective for climate mitigation by offsetting CO2 emissions. In future work, climate effects and management strategies regarding surface albedo at a multitude of spatiotemporal resolutions need to be systematically evaluated to promote its application in climate mitigation, where a life cycle assessment (LCA) method considering both climate benefits and side effects (e.g., thermal comfort) should be followed.

MODIS-observed spatiotemporal changes in surface albedo of Karakoram glaciers during 2000-2018

The role of albedo is very important in modulating the surface energy balance of glaciers. The main objective of this study is to assess the spatiotemporal variability in surface albedo of the Karakoram glaciers in Pakistan during the summer seasons (June, July and August) for the period from 2000-2018. We used Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate the amount of glacier surface albedo. We combined the MODIS Terra- and Aqua-derived albedo products to reduce the amount of cloud influence and to improve the estimation of glacier surface albedo. Our results indicate that the average annual decrease in albedo is ~0.041% during the summer. The decrease in albedo was relatively high during recent years, with an annual rate of decrease of ~0.45%. The decreasing trend in albedo is towards the north-western part of the Karakoram mountain range. Climate change is the potential cause of albedo variations in the study area. Albedo has a strong negative correlation with temperature (r = -0.811) and a strong positive correlation with precipitation (r = 0.809). The present study concludes that trend in decreasing albedo is higher during the recent years than the last decade and climate change is playing a vital role in it.

Multi-decadal analysis of high-resolution albedo changes induced by urbanization over contrasted Chinese cities based on Landsat data

Surface albedo is a key parameter in the surface energy balance and has been identified as a primary essential climate variable (ECV). Variations in surface albedo can be used as a diagnostic tool for local climate change. This is particularly true in urban areas, where the impacts of land cover conversion due to increasing anthropogenic demands can be examined using surface albedo changes. Most of the previous studies of albedo in cities have relied on coarse-resolution datasets with short time spans and have disregarded continuous monitoring. In addition, it is still unclear which urbanization processes are involved and what effects they have on surface albedo over long time periods. This study aimed to identify the contribution of increasing urbanization to the regional climate by analyzing spatial and temporal changes in surface albedo. Assigning albedo values to land cover types is useful for determining the level of transformation and their impacts in various Chinese cities that underwent specific evolutions between 1986 and 2018. The Direct Estimation (DE) approach was modified to estimate the daily mean surface albedo at 30 m based on Landsat observations. It resulted root-mean-square errors (RMSEs) of less than 0.044 and bias about 0.006 between observations and model estimations. Such accuracy obtained after correcting the orbital drift of the Landsat satellite, was deemed satisfactory for detecting potential changes in albedo. Major findings are: 1) A notable trend was found over the past 33 years of 11 major Chinese cities, i.e. population about 10 million and more, with a general albedo increase from satellite observations. The higher resolution Landsat dataset showed a trend 3 times larger than the Global Land Surface Satellites (GLASS) product, which outlines the need for analyzing high resolution imagery in priority for reliable estimate of albedo over heterogeneous urban landscapes. 2) An increase in albedo infers a negative radiative forcing at an average rate of −2.771 W/m2 per decade, thereby indicating a cooling effect for most Chinese cities. 3) Changes in surface albedo were also closely linked to landscape transformation, clearly observed using the 30 m resolution of the Landsat data. 4) Throughout the study period, surface albedo exhibited a temporal U-shaped curve in built-up areas under development, with albedo peaks in old and newly built districts and a decrease in albedo between the two eras.

Identifying the paths and contributions of climate impacts on the variation in land surface albedo over the Arctic

Understanding historical changes in land surface albedo (ALB) is critical for projecting future climate changes over the Arctic. Previous studies have reported the important role of snow cover and vegetation growth in regulating Arctic land ALB, but their main influencing paths and separate contributions remain underexamined. In this study, the partial least squares path model (PLS-PM) and random forest model (RFM) are adopted to explore the dominant paths of three climate variables—temperature (Tem), precipitation (Pre) and wet days (WD)—in influencing the interannual variations in ALB (IVA) by regulating vegetation and snow cover. The results show that ALB in October and December exhibit significant (p < 0.05) upward and downward trends, respectively. ALB in the Northwest Territories, Victoria Island and Nunavut of the Arctic significantly (p < 0.05) increase in summer and October. The summer snow cover fraction (SCF) shows a significant positive correlation with ALB (R: 0.60‒0.78), while significant negative (R = -0.60) and positive (R = 0.50) relations are found between the enhanced vegetation index (EVI) and ALB during July and September, respectively. According to PLS−PM, the dominant paths that affect the IVA exhibit a prominent seasonal feature. Pre → EVI → ALB and Tem → SCF → ALB are identified as the main paths for ALB changes during spring, while in summer, the only significant path is Tem → SCF → ALB. In autumn, nonsignificant impacts on the variations in EVI and SCF (p > 0.05) are observed, while a significant path WD → SCF → ALB is found in winter. Based on RFM, the relative contribution (RC) of vegetation change to March IVA can reach 52.1 ± 9.2%, while snow variations play a dominant role in April and May, with contributions of 57.8 ± 8.4% and 68.4 ± 4.1%, respectively. Seasonally, summer IVA is mainly impacted by snow (RC from 50.5 ± 4.5% to 65.9 ± 4.5%), while vegetation exerts the dominant influence on IVA in autumn except for September. In winter, however, the dominant strength of vegetation is weakened except in December, with IVA mainly controlled by snow (RC above 50%).

Effects of Dynamic Vegetation on Global Climate Simulation Using the NCEP GFS and SSiB4/TRIFFID

Two global experiments were carried out to investigate the effects of dynamic vegetation processes on numerical climate simulations from 1948 to 2008. The NCEP Global Forecast System (GFS) was coupled with a biophysical model, the Simplified Simple Biosphere Model (SSiB) version 2 (GFS/SSiB2), and it was also coupled with a biophysical and dynamic vegetation model, SSiB version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics (TRIFFID) (GFS/SSiB4/TRIFFID). The effects of dynamic vegetation processes on the simulation of precipitation, near-surface temperature, and the surface energy budget were identified on monthly and annual scales by assessing the GFS/SSiB4/TRIFFID and GFS/SSiB2 results against the satellite-derived leaf area index (LAI) and albedo and the observed land surface temperature and precipitation. The results show that compared with the GFS/SSiB2 model, the temporal correlation coefficients between the globally averaged monthly simulated LAI and the Global Inventory Monitoring and Modeling System (GIMMS)/Global Land Surface Satellite (GLASS) LAI in the GFS/SSiB4/TRIFFID simulation increased from 0.31/0.29 (SSiB2) to 0.47/0.46 (SSiB4). The correlation coefficients between the simulated and observed monthly mean near-surface air temperature increased from 0.50 (Africa), 0.35 (Southeast Asia), and 0.39 (South America) to 0.56, 0.41, and 0.44, respectively. The correlation coefficients between the simulated and observed monthly mean precipitation increased from 0.19 (Africa), 0.22 (South Asia), and 0.22 (East Asia) to 0.25, 0.27, and 0.28, respectively. The greatest improvement occurred over arid and semiarid areas. The spatiotemporal variability and changes in vegetation and ground surface albedo modeled by the GFS with a dynamic vegetation model were more consistent with the observations. The dynamic vegetation processes contributed to the surface energy and water balance and in turn, improved the annual variations in the simulated regional temperature and precipitation. The dynamic vegetation processes had the greatest influence on the spatiotemporal changes in the latent heat flux. This study shows that dynamic vegetation processes in earth system models significantly improve simulations of the climate mean status.

The influence of forest fire aerosol and air temperature on glacier albedo, western North America

Over the past decade, western North America glaciers experienced strong mass loss. Regional mass loss during the ablation season is influenced by air temperature, but the importance of other factors such as changes in surface albedo remains uncertain. We examine changes in surface albedo for 17 glaciated regions of western North America as documented in a 20-year record (2000 to 2019) of MODIS daily snow albedo (MOD10A1). Trend analysis reveals that albedo declined for 4% to 81% of the albedo grid cells, and the largest negative trends were situated south of 60°N and in the provinces of British Columbia and Alberta. Sen's slope estimates indicate that 15 of 17 regions showed a decline of which the majority of the largest declines occurred within 100 m of glacier median elevation, suggesting that these declines are driven by a rise of the transient snowline. For most regions, albedo correlates strongly to temperature, and albedo trend in the Chugach region of Alaska, the South Coast, Southern Interior and Central and Southern Rockies of Canada show a significant relationship to aerosols optical depth. Temperature is approximately 2–6 times more predictive of the variation in albedo than AOD for the majority of regions, except the Southern Interior and Southern Rockies where albedo shows a greater dependence on AOD. Investigation of broadband albedo (MCD43A3) for snow grid cells above glacier median elevation in the Central and Southern Rockies shows that declines in the visible and near infrared portions of the spectrum are linked to the presence of forest fire generated aerosols. The results of this study indicate that glacier surface mass balance experiences a regional dependence on forest fire generated light absorbing particles.

Understanding the surface temperature response and its uncertainty to CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;, black carbon, and sulfate

Abstract. Understanding the regional surface temperature responses to different anthropogenic climate forcing agents, such as greenhouse gases and aerosols, is crucial for understanding past and future regional climate changes. In modern climate models, the regional temperature responses vary greatly for all major forcing agents, but the causes of this variability are poorly understood. Here, we analyze how changes in atmospheric and oceanic energy fluxes due to perturbations in different anthropogenic climate forcing agents lead to changes in global and regional surface temperatures. We use climate model data on idealized perturbations in four major anthropogenic climate forcing agents (CO2, CH4, sulfate, and black carbon aerosols) from Precipitation Driver Response Model Intercomparison Project (PDRMIP) climate experiments for six climate models (CanESM2, HadGEM2-ES, NCAR-CESM1-CAM4, NorESM1, MIROC-SPRINTARS, GISS-E2). Particularly, we decompose the regional energy budget contributions to the surface temperature responses due to changes in longwave and shortwave fluxes under clear-sky and cloudy conditions, surface albedo changes, and oceanic and atmospheric energy transport. We also analyze the regional model-to-model temperature response spread due to each of these components. The global surface temperature response stems from changes in longwave emissivity for greenhouse gases (CO2 and CH4) and mainly from changes in shortwave clear-sky fluxes for aerosols (sulfate and black carbon). The global surface temperature response normalized by effective radiative forcing is nearly the same for all forcing agents (0.63, 0.54, 0.57, 0.61 K W−1 m2). While the main physical processes driving global temperature responses vary between forcing agents, for all forcing agents the model-to-model spread in temperature responses is dominated by differences in modeled changes in longwave clear-sky emissivity. Furthermore, in polar regions for all forcing agents the differences in surface albedo change is a key contributor to temperature responses and its spread. For black carbon, the modeled differences in temperature response due to shortwave clear-sky radiation are also important in the Arctic. Regional model-to-model differences due to changes in shortwave and longwave cloud radiative effect strongly modulate each other. For aerosols, clouds play a major role in the model spread of regional surface temperature responses. In regions with strong aerosol forcing, the model-to-model differences arise from shortwave clear-sky responses and are strongly modulated by combined temperature responses to oceanic and atmospheric heat transport in the models.

Assessing climatic benefits from forestation potential in semi-arid lands

Forestation actions are a major tool for both climate-change mitigation and biodiversity conservation. We address two weaknesses in this approach: the little attention given to the negative effects of reduced albedo associated with forestation in many regions, and ignoring the potential of drylands that account for 40% of the global potential land area for forestation. We propose an approach to identify suitable land for forestation and quantify its ‘net equivalent carbon stock change’ over 80 years of forest lifetime (NESC), accounting for both carbon sequestration and albedo changes. We combined remote-sensing tools with data-based estimates of surface parameters and with published climate matrices, to identify suitable land for forestation actions. We then calculated the cumulative (over 80 years) ‘net sequestration potential’ (ΔSP), the ‘emission equivalent of shortwave radiation forcing’ (EESF) due to changes in surface albedo, and, in turn, the combined NESC = ΔSP−EESF, of planting forests with >40% tree-cover. Demonstrating our approach in a large climatically diverse state (Queensland), we identified 14.5 million hectares of potential forestation land in its semi-arid land and show that accounting for the EESF, reduces the climatic benefits of the ΔSP by almost 50%. Nevertheless, it results in a total NESC of 0.72 Gt C accumulated by the end of the century, and 80 years of forestation cycle. This estimated NESC is equivalent to 15% of the projected carbon emissions for the same period in Queensland, for a scenario of no change in emission rates during that period. Our approach extends restoration efforts by identifying new land for forestation and carbon sequestration but also demonstrates the importance of quantifying the climatic value of forestation in drylands.