scholarly journals Contribution of Changes in Snow Cover Extent to Shortwave Radiation Perturbations at the Top of the Atmosphere over the Northern Hemisphere during 2000–2019

2021 ◽  
Vol 13 (23) ◽  
pp. 4938
Author(s):  
Xiaona Chen ◽  
Yaping Yang ◽  
Cong Yin
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Radiative Forcing ◽  
Snow Accumulation ◽  
Climate System ◽  
Shortwave Radiation ◽  
Climate Projections ◽  
Change Models ◽  
Snow Cover Extent

Snow-induced radiative forcing (SnRF), defined as the instantaneous perturbation of the Earth’s shortwave radiation at the top of the atmosphere (TOA), results from variations in the terrestrial snow cover extent (SCE), and is critical for the regulation of the Earth’s energy budget. However, with the growing seasonal divergence of SCE over the Northern Hemisphere (NH) in the past two decades, novel insights pertaining to SnRF are lacking. Consequently, the contribution of SnRF to TOA shortwave radiation anomalies still remains unclear. Utilizing the latest datasets of snow cover, surface albedo, and albedo radiative kernels, this study investigated the distribution of SnRF over the NH and explored its changes from 2000 to 2019. The 20-year averaged annual mean SnRF in the NH was −1.13 ± 0.05 W m−2, with a weakening trend of 0.0047 Wm−2 yr−1 (p < 0.01) during 2000–2019, indicating that an extra 0.094 W m−2 of shortwave radiation was absorbed by the Earth climate system. Moreover, changes in SnRF were highly correlated with satellite-observed TOA shortwave flux anomalies (r = 0.79, p < 0.05) during 2000–2019. Additionally, a detailed contribution analysis revealed that the SnRF in snow accumulation months, from March to May, accounted for 58.10% of the annual mean SnRF variability across the NH. These results can assist in providing a better understanding of the role of snow cover in Earth’s climate system in the context of climate change. Although the rapid SCE decline over the NH has a hiatus for the period during 2000–2019, SnRF continues to follow a weakening trend. Therefore, this should be taken into consideration in current climate change models and future climate projections.

scholarly journals 100 Years of Progress in Cloud Physics, Aerosols, and Aerosol Chemistry Research

2019 ◽  
Vol 59 ◽  
pp. 11.1-11.72 ◽  
Author(s):  
Sonia M. Kreidenweis ◽  
Markus Petters ◽  
Ulrike Lohmann
Keyword(s):  
Radiative Forcing ◽  
Cloud Microphysics ◽  
Climate System ◽  
Mitigation Strategies ◽  
Climate Projections ◽  
Weather Modification ◽  
Theoretical Understanding ◽  
Aerosol Cloud ◽  
Chemistry Research ◽  
Aerosol Cloud Interactions

Abstract This chapter reviews the history of the discovery of cloud nuclei and their impacts on cloud microphysics and the climate system. Pioneers including John Aitken, Sir John Mason, Hilding Köhler, Christian Junge, Sean Twomey, and Kenneth Whitby laid the foundations of the field. Through their contributions and those of many others, rapid progress has been made in the last 100 years in understanding the sources, evolution, and composition of the atmospheric aerosol, the interactions of particles with atmospheric water vapor, and cloud microphysical processes. Major breakthroughs in measurement capabilities and in theoretical understanding have elucidated the characteristics of cloud condensation nuclei and ice nucleating particles and the role these play in shaping cloud microphysical properties and the formation of precipitation. Despite these advances, not all their impacts on cloud formation and evolution have been resolved. The resulting radiative forcing on the climate system due to aerosol–cloud interactions remains an unacceptably large uncertainty in future climate projections. Process-level understanding of aerosol–cloud interactions remains insufficient to support technological mitigation strategies such as intentional weather modification or geoengineering to accelerating Earth-system-wide changes in temperature and weather patterns.

scholarly journals Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations

2013 ◽  
Vol 17 (10) ◽  
pp. 3921-3936 ◽  
Author(s):  
M. Ménégoz ◽  
H. Gallée ◽  
H. W. Jacobi
Keyword(s):  
Snow Cover ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
The Tibetan Plateau ◽  
Monsoon Period ◽  
Medium Range Weather Forecast ◽  
Snow Cover Extent ◽  
The Himalaya

Abstract. We applied a Regional Climate Model (RCM) to simulate precipitation and snow cover over the Himalaya, between March 2000 and December 2002. Due to its higher resolution, our model simulates a more realistic spatial variability of wind and precipitation than those of the reanalysis of the European Centre of Medium range Weather Forecast (ECMWF) used as lateral boundaries. In this region, we found very large discrepancies between the estimations of precipitation provided by reanalysis, rain gauges networks, satellite observations, and our RCM simulation. Our model clearly underestimates precipitation at the foothills of the Himalaya and in its eastern part. However, our simulation provides a first estimation of liquid and solid precipitation in high altitude areas, where satellite and rain gauge networks are not very reliable. During the two years of simulation, our model resembles the snow cover extent and duration quite accurately in these areas. Both snow accumulation and snow cover duration differ widely along the Himalaya: snowfall can occur during the whole year in western Himalaya, due to both summer monsoon and mid-latitude low pressure systems bringing moisture into this region. In Central Himalaya and on the Tibetan Plateau, a much more marked dry season occurs from October to March. Snow cover does not have a pronounced seasonal cycle in these regions, since it depends both on the quite variable duration of the monsoon and on the rare but possible occurrence of snowfall during the extra-monsoon period.

scholarly journals Fast Warming Has Accelerated Snow Cover Loss during Spring and Summer across the Northern Hemisphere over the Past 52 Years (1967–2018)

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 728
Author(s):  
Xuejiao Wu ◽  
Yongping Shen ◽  
Wei Zhang ◽  
Yinping Long
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Spatial Scales ◽  
Summer Precipitation ◽  
Temperature Changes ◽  
The Past ◽  
Four Seasons ◽  
Snow Cover Extent ◽  
Summer Temperatures ◽  
Substantial Decline

With snow cover changing worldwide in several worrisome ways, it is imperative to determine both the variability in snow cover in greater detail and its relationship with ongoing climate change. Here, we used the satellite-based snow cover extent (SCE) dataset of National Oceanic and Atmospheric Administration (NOAA) to detect SCE variability and its linkages to climate over the 1967–2018 periods across the Northern Hemisphere (NH). Interannually, the time series of SCE across the NH reveal a substantial decline in both spring and summer (−0.54 and −0.71 million km2/decade, respectively), and this decreasing trend corresponded with rising spring and summer temperatures over high-latitude NH regions. Among the four seasons, the temperature rise over the NH was the highest in winter (0.39 °C/decade, p < 0.01). More precipitation in winter was closely related to an increase of winter SCE in mid-latitude areas of NH. Summer precipitation over the NH increased at a significant rate (1.1 mm/decade, p < 0.01), which likely contribute to the accelerated reduction of summer’s SCE across the NH. However, seasonal sensitivity of SCE to temperature changes differed between the Eurasian and North American continents. Thus, this study provides a better understanding of seasonal SCE variability and climatic changes that occurred at regional and hemispheric spatial scales in the past 52 years.

The Effect of Climate Change on River Flow and Snow Cover in the NOPEX Area Simulated by a Simple Water Balance Model

1997 ◽  
Vol 28 (4-5) ◽  
pp. 273-282 ◽  
Author(s):  
C-Y Xu ◽  
Sven Halldin
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Snow Cover ◽  
Annual Runoff ◽  
Snow Accumulation ◽  
Water Balance Model ◽  
Annual Precipitation ◽  
Balance Model ◽  
Climate Change Scenarios ◽  
Average Annual Runoff

Within the next few decades, changes in global temperature and precipitation patterns may appear, especially at high latitudes. A simple monthly water-balance model of the NOPEX basins was developed and used for the purposes of investigating the effects on water availability of changes in climate. Eleven case study catchments were used together with a number of climate change scenarios. The effects of climate change on average annual runoff depended on the ratio of average annual runoff to average annual precipitation, with the greatest sensitivity in the catchments with lowest runoff coefficients. A 20% increase in annual precipitation resulted in an increase in annual runoff ranging from 31% to 51%. The greatest changes in monthly runoff were in winter (from December to March) whereas the smallest changes were found in summer. The time of the highest spring flow changed from April to March. An increase in temperature by 4°C greatly shortened the time of snow cover and the snow accumulation period. The maximum amount of snow during these short winters diminished by 50% for the NOPEX area even with an assumed increase of total precipitation by 20%.

scholarly journals Spatiotemporal Variability of Land Surface Albedo over the Tibet Plateau from 2001 to 2019

2020 ◽  
Vol 12 (7) ◽  
pp. 1188
Author(s):  
Xingwen Lin ◽  
Jianguang Wen ◽  
Qinhuo Liu ◽  
Dongqin You ◽  
Shengbiao Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Snow Cover ◽  
Land Surface ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Tibet Plateau ◽  
Land Cover Types ◽  
Yearly Average

As an essential climate variable (ECV), land surface albedo plays an important role in the Earth surface radiation budget and regional or global climate change. The Tibetan Plateau (TP) is a sensitive environment to climate change, and understanding its albedo seasonal and inter-annual variations is thus important to help capture the climate change rules. In this paper, we analyzed the large-scale spatial patterns, temporal trends, and seasonal variability of land surface albedo overall the TP, based on the moderate resolution imaging spectroradiometer (MODIS) MCD43 albedo products from 2001 to 2019. Specifically, we assessed the correlations between the albedo anomaly and the anomalies of normalized difference vegetation index (NDVI), the fraction of snow cover (snow cover), and land surface temperature (LST). The results show that there are larger albedo variations distributed in the mountainous terrain of the TP. Approximately 10.06% of the land surface is identified to have been influenced by the significant albedo variation from the year 2001 to 2019. The yearly averaged albedo was decreased significantly at a rate of 0.0007 (Sen’s slope) over the TP. Additionally, the yearly average snow cover was decreased at a rate of 0.0756. However, the yearly average NDVI and LST were increased with slopes of 0.0004 and 0.0253 over the TP, respectively. The relative radiative forcing (RRF) caused by the land cover change (LCC) is larger than that caused by gradual albedo variation in steady land cover types. Overall, the RRF due to gradual albedo variation varied from 0.0005 to 0.0170 W/m2, and the RRF due to LCC variation varied from 0.0037 to 0.0243 W/m2 during the years 2001 to 2019. The positive RRF caused by gradual albedo variation or the LCC can strengthen the warming effects in the TP. The impact of the gradual albedo variations occurring in the steady land cover types was very low between 2001 and 2019 because the time series was short, and it therefore cannot be neglected when examining radiative forcing for a long time series regarding climate change.

Implications of intrinsic variability for economic assessments of climate change

2020 ◽  
Author(s):  
David Stainforth ◽  
Raphael Calel ◽  
Sandra Chapman ◽  
Nicholas Watkins
Keyword(s):  
Climate Change ◽  
Integrated Assessment ◽  
Radiative Forcing ◽  
Epistemic Uncertainty ◽  
Global Climate Model ◽  
Damage Function ◽  
Climate System ◽  
Social Planner ◽  
Intrinsic Variability ◽  
Integrated Assessment Models

&lt;p&gt;Integrated Assessment Models (IAMs) are widely used to evaluate the economic costs of climate change, the social cost of carbon and the value of mitigation policies. These IAMs include simple energy balance models (EBMs) to represent the physical climate system and to calculate the timeseries of global mean temperature in response to changing radiative forcing[1]. The EBMs are deterministic in nature which leads to smoothly varying GMT trajectories so for simple monotonically increasing forcing scenarios (e.g. representative concentration pathways (RCPs) 8.5, 6.0 and 4.5) the GMT trajectories are also monotonically increasing. By contrast real world, and global-climate-model-derived, timeseries show substantial inter-annual and inter-decadal variability. Here we present an analysis of the implications of this intrinsic variability for the economic consequences of climate change.&lt;/p&gt;&lt;p&gt;We use a simple stochastic EBM to generate large ensembles of GMT trajectories under each of the RCP forcing scenarios. The damages implied by each trajectory are calculated using the Weitzman damage function. This provides a conditional estimate of the unavoidable uncertainty in implied damages. It turns out to be large and positively skewed due to the shape of the damage function. Under RCP2.6 we calculate a 5-95% range of -30% to +52% of the deterministic value; -13% to +16% under RCP 8.5. The risk premia associated with such unavoidable uncertainty are also significant. Under our economic assumptions a social planner would be willing to pay 32 trillion dollars to avoid just the intrinsic uncertainty in RCP8.5. This figure rises further when allowance is made for epistemic uncertainty in relation to climate sensitivity. We conclude that appropriate representation of stochastic variability in the climate system is important to include in future economic assessments of climate change.&lt;/p&gt;&lt;p&gt;&lt;br&gt;[1] Calel, R. and Stainforth D.A., &amp;#8220;On the Physics of Three Integrated Assessment Models&amp;#8221;, Bulletin of the American Meteorological Society, 2017.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

A Physically Based Daily Hydrometeorological Model for Complex Mountain Terrain

2009 ◽  
Vol 10 (6) ◽  
pp. 1430-1446 ◽  
Author(s):  
Ryan J. MacDonald ◽  
James M. Byrne ◽  
Stefan W. Kienzle
Keyword(s):  
Snow Cover ◽  
Estimation Method ◽  
Snow Accumulation ◽  
Lapse Rate ◽  
Effect Of Temperature ◽  
Systems Science ◽  
River Watershed ◽  
Temperature Lapse Rate ◽  
Snow Cover Extent ◽  
Physically Based

Abstract This paper describes the continued development of the physically based hydrometeorological model Generate Earth Systems Science input (GENESYS) and its application in simulating snowpack in the St. Mary (STM) River watershed, Montana. GENESYS is designed to operate a high spatial and temporal resolution over complex mountainous terrain. The intent of this paper is to assess the performance of the model in simulating daily snowpack and the spatial extent of snow cover over the St. Mary River watershed. A new precipitation estimation method that uses snowpack telemetry (SNOTEL) and snow survey data is presented and compared with two other methods, including Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation data. A method for determining daily temperature lapse rates from NCEP reanalysis data is also presented and the effect of temperature lapse rate on snowpack simulations is determined. Simulated daily snowpack values compare well with observed values at the Many Glacier SNOTEL site, with varying degrees of accuracy, dependent on the method used to estimate precipitation. The spatial snow cover extent compares well with Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products for three dates selected to represent snow accumulation and ablation periods.

scholarly journals A temporally consistent 8-day 0.05° gap-free snow cover extent dataset over the Northern Hemisphere for the period 1981–2019

2021 ◽  
Author(s):  
Xiaona Chen ◽  
Shunlin Liang ◽  
Lian He ◽  
Yaping Yang ◽  
Cong Yin
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Land Surface ◽  
Glass Product ◽  
Aggregation Process ◽  
Climate Data ◽  
Important Indicator ◽  
Continental Scale ◽  
Snow Cover Extent ◽  
Frequency Monitoring

Abstract. Northern Hemisphere (NH) snow cover extent (SCE) is one of the most important indicator of climate change due to its unique surface property. However, short temporal coverage, coarse spatial resolution, and different snow discrimination approach among existing continental scale SCE products hampers its detailed studies. Using the latest Advanced Very High Resolution Radiometer Surface Reflectance (AVHRR-SR) Climate Data Record (CDR) and several ancillary datasets, this study generated a temporally consistent 8-day 0.05° gap-free SCE covering the NH landmass for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite. The development of GLASS SCE contains five steps. First, a decision tree algorithm with multiple threshold tests was applied to distinguish snow cover (NHSCE-D) with other land cover types from daily AVHRR-SR CDR. Second, gridcells with cloud cover and invalid observations were filled by two existing daily SCE products. The gap-filled gridcells were further merged with NHSCE-D to generate combined daily SCE over the NH (NHSCE-Dc). Third, an aggregation process was used to detect the maximum SCE and minimum gaps in each 8-day periods from NHSCE-Dc. Forth, the gaps after aggregation process were further filled by the climatology of snow cover probability to generate the gap-free GLASS SCE. Fifth, the validation process was carried out to evaluate the quality of GLASS SCE. Validation results by using 562 Global Historical Climatology Network stations during 1981–2017 (r = 0.61, p < 0.05) and MOD10C2 during 2001–2019 (r = 0.97, p < 0.01) proved that the GLASS SCE product is credible in snow cover frequency monitoring. Moreover, cross-comparison between GLASS SCE and surface albedo during 1982–2018 further confirmed its values in climate changes studies. The GLASS SCE data are available at https://doi.org/10.5281/zenodo.5775238 (Chen et al. 2021).

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