Arctic Surface, Cloud, and Radiation Properties Based on the AVHRR Polar Pathfinder Dataset. Part II: Recent Trends

2005 ◽  
Vol 18 (14) ◽  
pp. 2575-2593 ◽  
Author(s):  
Xuanji Wang ◽  
Jeffrey R. Key
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Time Scale ◽  
Cloud Fraction ◽  
Annual Rate ◽  
The Arctic ◽  
Surface Temperature Change ◽  
Annual Time Scale ◽  
Annual Time ◽  
Arctic Surface

Abstract Over the past 20 yr, some Arctic surface and cloud properties have changed significantly. Results of an analysis of satellite data show that the Arctic has warmed and become cloudier in spring and summer but has cooled and become less cloudy in winter. The annual rate of surface temperature change is 0.057°C for the Arctic region north of 60°N. The surface broadband albedo has decreased significantly in autumn, especially over the Arctic Ocean, indicating a later freeze-up and snowfall. The surface albedo has decreased at an annual rate of −0.15% (absolute). Cloud fraction has decreased at an annual rate of −0.6% (absolute) in winter and increased at annual rates of 0.32% and 0.16% in spring and summer, respectively. On an annual time scale, there is no trend in cloud fraction. During spring and summer, changes in sea ice albedo that result from surface warming tend to modulate the radiative effect of increasing cloud cover. On an annual time scale, the all-wave cloud forcing at the surface has decreased at an annual rate of –0.335 W m−2, indicating an increased cooling by clouds. There are large correlations between surface temperature anomalies and climate indices such as the Arctic Oscillation (AO) index for some areas, implying linkages between global climate change and Arctic climate change.

scholarly journals Arctic Climate Variability and Trends from Satellite Observations

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Xuanji Wang ◽  
Jeffrey Key ◽  
Yinghui Liu ◽  
Charles Fowler ◽  
James Maslanik ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
Surface Temperature ◽  
Cloud Fraction ◽  
Arctic Sea Ice ◽  
Satellite Observations ◽  
Arctic Climate ◽  
The Arctic ◽  
Climate Indices ◽  
Surface Warming

Arctic climate has been changing rapidly since the 1980s. This work shows distinctly different patterns of change in winter, spring, and summer for cloud fraction and surface temperature. Satellite observations over 1982–2004 have shown that the Arctic has warmed up and become cloudier in spring and summer, but cooled down and become less cloudy in winter. The annual mean surface temperature has increased at a rate of 0.34°C per decade. The decadal rates of cloud fraction trends are −3.4%, 2.3%, and 0.5% in winter, spring, and summer, respectively. Correspondingly, annually averaged surface albedo has decreased at a decadal rate of −3.2%. On the annual average, the trend of cloud forcing at the surface is −2.11 W/m2per decade, indicating a damping effect on the surface warming by clouds. The decreasing sea ice albedo and surface warming tend to modulate cloud radiative cooling effect in spring and summer. Arctic sea ice has also declined substantially with decadal rates of −8%, −5%, and −15% in sea ice extent, thickness, and volume, respectively. Significant correlations between surface temperature anomalies and climate indices, especially the Arctic Oscillation (AO) index, exist over some areas, implying linkages between global climate change and Arctic climate change.

scholarly journals Monitoring Land Surface Temperature Change with Landsat Images during Dry Seasons in Bac Binh, Vietnam

2020 ◽  
Vol 12 (24) ◽  
pp. 4067
Author(s):  
Thanhtung Dang ◽  
Peng Yue ◽  
Felix Bachofer ◽  
Michael Wang ◽  
Mingda Zhang
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Dry Season ◽  
Landsat Images ◽  
Surface Temperature Change ◽  
Green Vegetation ◽  
Dry Seasons

Global warming-induced climate change evolved to be one of the most important research topics in Earth System Sciences, where remote sensing-based methods have shown great potential for detecting spatial temperature changes. This study utilized a time series of Landsat images to investigate the Land Surface Temperature (LST) of dry seasons between 1989 and 2019 in the Bac Binh district, Binh Thuan province, Vietnam. Our study aims to monitor LST change, and its relationship to land-cover change during the last 30 years. The results for the study area show that the share of Green Vegetation coverage has decreased rapidly for the dry season in recent years. The area covered by vegetation shrank between 1989 and 2019 by 29.44%. Our findings show that the LST increase and decrease trend is clearly related to the change of the main land-cover classes, namely Bare Land and Green Vegetation. For the same period, we find an average increase of absolute mean LST of 0.03 °C per year for over thirty years across all land-cover classes. For the dry season in 2005, the LST was extraordinarily high and the area with a LST exceeding 40 °C covered 64.10% of the total area. We expect that methodological approach and the findings can be applied to study change in LST, land-cover, and can contribute to climate change monitoring and forecasting of impacts in comparable regions.

scholarly journals Attribution Analysis of Long-Term Trends of Aridity Index in the Huai River Basin, Eastern China

2020 ◽  
Vol 12 (5) ◽  
pp. 1743
Author(s):  
Meng Li ◽  
Ronghao Chu ◽  
Abu Reza Md. Towfiqul Islam ◽  
Yuelin Jiang ◽  
Shuanghe Shen
Keyword(s):  
River Basin ◽  
Time Scale ◽  
Aridity Index ◽  
Growing Season ◽  
Dominant Factor ◽  
Annual Time Scale ◽  
Huai River ◽  
Autumn And Winter ◽  
Annual Time ◽  
The Huai River Basin

This paper aims to combinedly investigate the spatiotemporal trends of precipitation (Pre), reference evapotranspiration (ET0), and aridity index (AI) by employing nonparametric methods based on daily datasets from 137 meteorological stations during 1961–2014 in the Huai River Basin (HRB). The dominant factors influencing ET0 and AI trends were also explored using the detrended and differential equation methods. Results show that (1) Pre, ET0, and AI were much larger in summer than in other seasons, and AI had a nonsignificant increasing trend in annual time scale, while Pre and ET0 exhibited decreasing trends, but AI showed a downward trend in spring and autumn (becoming drier) and an upward trend during summer and winter due to increased Pre (becoming wetter); (2) lower AI values were identified in north and higher in south, and lower ET0 was identified in south and higher in north in annual time scale, growing season and spring, while ET0 decreased from west to east in summer and winter, the spatial distribution of Pre was similar to that of AI; (3) for ET0 trends, in general, wind speed at two-meter height (u2) was the dominant factor in spring, autumn, winter, and annual time scale, while in other seasons, solar radiation (Rs) played a dominant role; (4) for AI trends, AI was mostly contributed by Pre in spring, autumn, and winter, the Rs contributed the most to AI trend in growing season and summer, then in annual time scale, u2 was the dominant factor; (5) overall, the contribution of Pre to AI trends was much larger than that of ET0 in spring, autumn, and winter, while AI was mostly contributed by ET0 in annual time scale, growing season and summer. The outcomes of the study may improve our scientific understanding of recent climate change effects on dry–wet variations in the HRB; moreover, this information may be utilized in other climatic regions for comparison analyses.

scholarly journals TWIG: A model to simulate the gravitropic response of a tree axis in the frame of elasticity and viscoelasticity, at intra-annual time scale

2011 ◽  
Vol 273 (1) ◽  
pp. 115-129 ◽  
Author(s):  
Catherine Coutand ◽  
Jean-Denis Mathias ◽  
Georges Jeronimidis ◽  
Jean-François Destrebecq
Keyword(s):  
Time Scale ◽  
Gravitropic Response ◽  
Annual Time Scale ◽  
Annual Time

scholarly journals Different Pathways to an Early Eocene Climate

2021 ◽  
Author(s):  
Matthew Henry ◽  
Geoffrey Vallis
Keyword(s):  
Heat Capacity ◽  
Surface Temperature ◽  
Land Surface ◽  
Co2 Concentration ◽  
Surface Heat ◽  
The Arctic ◽  
Early Eocene ◽  
Surface Temperature Change ◽  
High Co2 ◽  
Co2 Levels

The early Eocene was characterised by much higher temperatures and a smaller equator-to-pole surface temperature gradient than today. Comprehensive climate models have been reasonably successful in simulating many features of that climate in the annual average. However, good simulations of the seasonal variations, and in particular the much reduced Arctic land temperature seasonality and associated much warmer winters, have proven more difficult. Further, aside from an increased level of greenhouse gases, it remains unclear what the key processes are that give rise to an Eocene climate, and whether there is a unique combination of factors that leads to agreement with available proxies. Here we use a very flexible General Circulation Model to examine the sensitivity of the modelled climate to differences in CO2 concentration, land surface properties, ocean heat transport, and cloud extent and thickness. Even in the absence of ice or changes in cloudiness, increasing the CO2 concentration leads to a polar-amplified surface temperature change because of increased water vapour and the lack of convection at high latitudes. Additional low clouds over Arctic land generally decreases summer temperatures and, except at very high CO2 levels, increases winter temperatures, thus helping achieve an Eocene climate. An increase in the land surface heat capacity, plausible given large changes in vegetation and landscape, also decreases the Arctic land seasonality. In general, various different combinations of factors -- high CO2 levels, changes in low-level clouds, and an increase in land surface heat capacity -- can lead to a simulation consistent with current proxy data.

scholarly journals Estimating the flood frequency distribution at seasonal and annual time scale

2012 ◽  
Vol 9 (6) ◽  
pp. 7947-7967 ◽  
Author(s):  
E. Baratti ◽  
A. Montanari ◽  
A. Castellarin ◽  
J. L. Salinas ◽  
A. Viglione ◽  
...  
Keyword(s):  
Objective Function ◽  
Frequency Analysis ◽  
Time Scale ◽  
Peak Flow ◽  
Flood Frequency ◽  
Cumulative Distribution ◽  
Annual Time Scale ◽  
Frequency Regime ◽  
Seasonal Flood ◽  
Annual Time

Abstract. We propose an original approach to infer the flood frequency distribution at seasonal and annual time scale. Our purpose is to estimate the peak flow that is expected for an assigned return period T, independently of the season in which it occurs (i.e. annual flood frequency regime), as well as in different selected sub-yearly periods (i.e. seasonal flood frequency regime). While a huge literature exists on annual flood frequency analysis, few studies have focused on the estimation of seasonal flood frequencies despite the relevance of the issue, for instance when scheduling along the months of the year the construction phases of river engineering works directly interacting with the active river bed, like for instance dams. An approximate method for joint frequency analysis is presented here that guarantees consistency between fitted annual and seasonal distributions, i.e. the annual cumulative distribution is the product of the seasonal cumulative distribution functions, under the assumption of independence among floods in different seasons. In our method the parameters of the seasonal frequency distributions are fitted by maximising an objective function that accounts for the likelihoods of both seasonal and annual peaks. Differently from previous studies, our procedure is conceived to allow the users to introduce subjective weights to the components of the objective function in order to emphasize the fitting of specific seasons or of the annual peak flow distribution. An application to the time series of the Blue Nile daily flows at Sudan-Ethiopia border is presented.

scholarly journals Present and future aerosol impacts on Arctic climate change in the GISS-E2.1 Earth system model

2021 ◽  
Vol 21 (13) ◽  
pp. 10413-10438
Author(s):  
Ulas Im ◽  
Kostas Tsigaridis ◽  
Gregory Faluvegi ◽  
Peter L. Langen ◽  
Joshua P. French ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Impacts ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Anthropogenic Aerosols ◽  
Emission Reductions ◽  
Air Temperatures ◽  
Arctic Surface

Abstract. The Arctic is warming 2 to 3 times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990–2014) and future (2015–2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60∘ N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases, while global annual mean greenhouse gas concentrations were prescribed and kept fixed in all simulations. Results showed that the simulations have underestimated observed surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), by more than 50 %, with the smallest biases calculated for the atmosphere-only simulations, where winds are nudged to reanalysis data. CMIP6 simulations performed slightly better in reproducing the observed surface aerosol concentrations and climate parameters, compared to the Eclipse simulations. In addition, simulations where atmosphere and ocean are fully coupled had slightly smaller biases in aerosol levels compared to atmosphere-only simulations without nudging. Arctic BC, organic aerosol (OA), and SO42- burdens decrease significantly in all simulations by 10 %–60 % following the reductions of 7 %–78 % in emission projections, with the Eclipse ensemble showing larger reductions in Arctic aerosol burdens compared to the CMIP6 ensemble. For the 2030–2050 period, the Eclipse ensemble simulated a radiative forcing due to aerosol–radiation interactions (RFARI) of -0.39±0.01 W m−2, which is −0.08 W m−2 larger than the 1990–2010 mean forcing (−0.32 W m−2), of which -0.24±0.01 W m−2 was attributed to the anthropogenic aerosols. The CMIP6 ensemble simulated a RFARI of −0.35 to −0.40 W m−2 for the same period, which is −0.01 to −0.06 W m−2 larger than the 1990–2010 mean forcing of −0.35 W m−2. The scenarios with little to no mitigation (worst-case scenarios) led to very small changes in the RFARI, while scenarios with medium to large emission mitigations led to increases in the negative RFARI, mainly due to the decrease in the positive BC forcing and the decrease in the negative SO42- forcing. The anthropogenic aerosols accounted for −0.24 to −0.26 W m−2 of the net RFARI in 2030–2050 period, in Eclipse and CMIP6 ensembles, respectively. Finally, all simulations showed an increase in the Arctic surface air temperatures throughout the simulation period. By 2050, surface air temperatures are projected to increase by 2.4 to 2.6 ∘C in the Eclipse ensemble and 1.9 to 2.6 ∘C in the CMIP6 ensemble, compared to the 1990–2010 mean. Overall, results show that even the scenarios with largest emission reductions leads to similar impact on the future Arctic surface air temperatures and sea-ice extent compared to scenarios with smaller emission reductions, implying reductions of greenhouse emissions are still necessary to mitigate climate change.

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