scholarly journals Climate Change after Tropical Deforestation: Seasonal Variability of Surface Albedo and Its Effects on Precipitation Change

2003 ◽  
Vol 16 (12) ◽  
pp. 2099-2104 ◽  
Author(s):  
Meire L. C. Berbet ◽  
Marcos Heil Costa
Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 112
Author(s):  
Roberto Salzano ◽  
Christian Lanconelli ◽  
Giulio Esposito ◽  
Marco Giusto ◽  
Mauro Montagnoli ◽  
...  

Polar areas are the most sensitive targets of climate change. From this perspective, the continuous monitoring of the cryosphere represents a critical need, which, now, we can only partially supply with specific satellite missions. The integration between remote-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behavior of snow, at different wavelengths, provides significant information about the microphysical characteristics of the surface in addition to the spatial distribution of snow/ice covers. This work presents the unmanned apparatus installed at Ny Ålesund (Svalbard) that provides continuous spectral surface albedo. A narrow band device was compared to a full-range system, to remotely sensed data during the 2015 spring/summer period at the Amundsen-Nobile Climate Change Tower. The system was integrated with a camera aimed to acquire sky and ground images. The results confirmed the possibility of making continuous observations of the snow surface and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period.


Author(s):  
Gerald L. Potter ◽  
Hugh W. Ellsaesser ◽  
Michael C. MacCracken ◽  
James S. Ellis ◽  
Frederick M. Luther

2020 ◽  
Vol 10 (19) ◽  
pp. 6878
Author(s):  
Ammara Nusrat ◽  
Hamza Farooq Gabriel ◽  
Sajjad Haider ◽  
Shakil Ahmad ◽  
Muhammad Shahid ◽  
...  

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.


1993 ◽  
Vol 98 (D4) ◽  
pp. 7289-7315 ◽  
Author(s):  
A. Henderson-Sellers ◽  
R. E. Dickinson ◽  
T. B. Durbidge ◽  
P. J. Kennedy ◽  
K. McGuffie ◽  
...  

2019 ◽  
Vol 32 (3) ◽  
pp. 897-916 ◽  
Author(s):  
Qing Yan ◽  
Ting Wei ◽  
Zhongshi Zhang

Simulations of past warm climate provide an opportunity to better understand how the climate system may respond to increased greenhouse gas emissions. Using the ~25-km-resolution Community Atmosphere Model, version 4 (CAM4), we examine climate change over China in the Late Pliocene warm period (3.264–3.025 Ma) and further explore the influences of different sea surface temperature (SST) forcings and model horizontal resolutions. Initial evaluation shows that the high-resolution CAM4 performs well in capturing the climatological distribution of present-day temperature, precipitation, and low-level monsoon circulations over China. Based on the standard Pliocene Research, Interpretation and Synoptic Mapping (version 4; PRISM4) boundary conditions, CAM4 predicts an increase of annual mean temperature by ~0.5°C over China in the Late Pliocene relative to the preindustrial era, with the greatest warming in northwest China but cooling in southwest China. Enhanced annual mean precipitation is observed in the Late Pliocene over most of China except for northwest China where precipitation is decreased. The East Asian summer (winter) monsoon is intensified (weakened) in the Late Pliocene as suggested by geological evidence, which is attributed to the enhanced (reduced) land–sea thermal contrast. The East Asian monsoon domain exhibits a northwestward expansion in the Late Pliocene, especially over the Tibetan Plateau. Additionally, our results indicate that the modeled climate change is sensitive to the Late Pliocene SST forcings and model resolution. Particularly, different SST forcings [PRISM4-based vs Pliocene Model Intercomparison Project (PlioMIP)-based SSTs] affect the modeled phase change of summer monsoon and the associated precipitation change, while model resolution (~25 vs 400 km) mainly impacts precipitation change.


2020 ◽  
Vol 12 (16) ◽  
pp. 2596
Author(s):  
Jorge Sánchez-Zapero ◽  
Fernando Camacho ◽  
Enrique Martínez-Sánchez ◽  
Roselyne Lacaze ◽  
Dominique Carrer ◽  
...  

The Copernicus Climate Change Service (C3S) includes estimates of Essential Climate Variables (ECVs) as a series of Climate Data Records (CDRs) derived from satellite data. The C3S Surface Albedo (SA) v1.0 CDR is composed of observations from National Oceanic and Atmospheric Administration (NOAA) Very High Resolution Radiometers (AVHRR) (1981–2005), and VEGETATION sensors onboard Satellites for the Observation of the Earth (SPOT/VGT) (1998–2014) and Project for Onboard Autonomy satellite (PROBA-V) (2014–2020), and will continue with Sentinel-3 (from 2020 onwards). The goal of this study is to assess the uncertainties associated with the C3S PROBA-V SA v1.0 product, with a focus on the transition from SPOT/VGT to PROBA-V. The methodology followed the good practices recommended by the Land Product Validation sub-group (LPV) of the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) for the validation of satellite-derived global albedo products. Several performance criteria were evaluated, including an intercomparison with National Aeronautics and Space Agency (NASA) MCD43A3 C6 products. C3S PROBA-V SA v1.0 and MCD43A3 C6 showed similar completeness but had higher fractions of missing data than C3S SPOT/VGT SA v1.0. C3S PROBA-V SA v1.0 showed similar precision (~1%) to MCD43A3 C6, improving the results of SPOT/VGT SA v1.0 (2–3%), but C3S PROBA-V SA v1.0 provided residual noise in the near-infrared (NIR). Good spatio-temporal continuity between C3S PROBA-V and SPOT/VGT SA v1.0 products was found with a mean bias between ±2%. The comparison with MCD43A3 C6 showed positive mean biases (5%, 8%, and 12% for visible, NIR and total shortwave, respectively). The accuracy assessment with ground measurements showed a median error of 18.4% with systematic overestimation (positive bias of 11.5%). The percentage of PROBA-V retrievals complying with the C3S target requirements was 28.6%.


2006 ◽  
Vol 19 (11) ◽  
pp. 2617-2630 ◽  
Author(s):  
Xin Qu ◽  
Alex Hall

Abstract In this paper, the two factors controlling Northern Hemisphere springtime snow albedo feedback in transient climate change are isolated and quantified based on scenario runs of 17 climate models used in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The first factor is the dependence of planetary albedo on surface albedo, representing the atmosphere's attenuation effect on surface albedo anomalies. It is potentially a major source of divergence in simulations of snow albedo feedback because of large differences in simulated cloud fields in Northern Hemisphere land areas. To calculate the dependence, an analytical model governing planetary albedo was developed. Detailed validations of the analytical model for two of the simulations are shown, version 3 of the Community Climate System Model (CCSM3) and the Geophysical Fluid Dynamics Laboratory global coupled Climate Model 2.0 (CM2.0), demonstrating that it facilitates a highly accurate calculation of the dependence of planetary albedo on surface albedo given readily available simulation output. In all simulations it is found that surface albedo anomalies are attenuated by approximately half in Northern Hemisphere land areas as they are transformed into planetary albedo anomalies. The intermodel standard deviation in the dependence of planetary albedo on surface albedo is surprisingly small, less than 10% of the mean. Moreover, when an observational estimate of this factor is calculated by applying the same method to the satellite-based International Satellite Cloud Climatology Project (ISCCP) data, it is found that most simulations agree with ISCCP values to within about 10%, despite further disagreements between observed and simulated cloud fields. This suggests that even large relative errors in simulated cloud fields do not result in significant error in this factor, enhancing confidence in climate models. The second factor, related exclusively to surface processes, is the change in surface albedo associated with an anthropogenically induced temperature change in Northern Hemisphere land areas. It exhibits much more intermodel variability. The standard deviation is about ⅓ of the mean, with the largest value being approximately 3 times larger than the smallest. Therefore this factor is unquestionably the main source of the large divergence in simulations of snow albedo feedback. To reduce the divergence, attention should be focused on differing parameterizations of snow processes, rather than intermodel variations in the attenuation effect of the atmosphere on surface albedo anomalies.


2020 ◽  
Vol 51 (5) ◽  
pp. 976-993
Author(s):  
Yuhui Yan ◽  
Baolin Xue ◽  
Yinglan A ◽  
Wenchao Sun ◽  
Hanwen Zhang

Abstract Quantification of runoff change is vital for water resources management, especially in arid or semiarid areas. This study used the Soil and Water Assessment Tool (SWAT) distributed hydrological model to simulate runoff in the upper reaches of the Hailar Basin (NE China) and to analyze quantitatively the impacts of climate change and land-use change on runoff by setting different scenarios. Two periods, i.e., the reference period (before 1988) and the interference period (after 1988), were identified based on long-term runoff datasets. In comparison with the reference period, the contribution rates of both climate change and land-use change to runoff change in the Hailar Basin during the interference period were 83.58% and 16.42%, respectively. The simulation analysis of climate change scenarios with differential precipitation and temperature changes suggested that runoff changes are correlated positively with precipitation change and that the impact of precipitation change on runoff is stronger than that of temperature. Under different economic development scenarios adopted, land use was predicted to have a considerable impact on runoff. The expansion of forests within the basin might induce decreased runoff owing to enhanced evapotranspiration.


2020 ◽  
Vol 12 (7) ◽  
pp. 1188
Author(s):  
Xingwen Lin ◽  
Jianguang Wen ◽  
Qinhuo Liu ◽  
Dongqin You ◽  
Shengbiao Wu ◽  
...  

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.


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