scholarly journals An Initial Study on Climate Change Fingerprinting Using the Reflected Solar Spectra

2016 ◽  
Vol 29 (8) ◽  
pp. 2781-2796 ◽  
Author(s):  
Zhonghai Jin ◽  
Moguo Sun
Keyword(s):  
Climate Change ◽  
Optical Depth ◽  
A Priori ◽  
Initial Study ◽  
Spectral Variation ◽  
Climate Variables ◽  
Cloud Optical Depth ◽  
Spatial And Temporal Scales ◽  
Retrieval Accuracy ◽  
Clear Sky

Abstract Attribution of averaged spectral variation over large spatial and temporal scales to different climate variables is central to climate change fingerprinting. Using 10 years of satellite data for simulation, the authors generate a group of observation-based spectral fingerprints and a time series of monthly mean reflectance spectra over the ocean in five large latitude regions and globally. Next, these fingerprints and the interannual variation spectra are used to retrieve the interannual changes in the relevant climate variables to test the concept of using the spectral fingerprinting approach for climate change attribution. Comparing the fingerprinting retrieval of climate variable change to the actual underlying variable change, the RMS differences between the two are less than twice as large as the monthly variability for all variables in all regions. Instances where larger errors are observed correspond to those variables with large nonlinear radiative response, such as the cloud optical depth and the ice particle size. Using the linear fingerprinting approach and accounting for the nonlinear radiative error in fingerprints results in significantly higher retrieval accuracy; the RMS errors are reduced to less than the monthly variability for nearly all variables, indicating the profound impact of the nonlinear error on fingerprinting retrieval. Another important finding is that if the cloud fraction is known a priori, the retrieval accuracy in cloud optical depth would be improved substantially. Moreover, a better retrieval for the water vapor amount and aerosol optical depth can be achieved from the clear-sky data only. The test results demonstrate that climate change fingerprinting based on reflected solar benchmark spectra is possible.

scholarly journals Development of CO2 Band-Based Cloud Emission and Scattering Indices and Their Applications to FY-3D Hyperspectral Infrared Atmospheric Sounder

2020 ◽  
Vol 12 (24) ◽  
pp. 4171
Author(s):  
Xinlu Xia ◽  
Xiaolei Zou
Keyword(s):  
Brightness Temperature ◽  
Optical Depth ◽  
Co2 Absorption ◽  
Cloud Detection ◽  
Detection Scheme ◽  
Cloud Optical Depth ◽  
Ice Cloud ◽  
Clear Sky ◽  
Temperature Observations ◽  
Cloud Top Pressure

The Hyperspectral Infrared Atmospheric Sounder (HIRAS) onboard the Feng Yun-3D (FY-3D) satellite is the first Chinese hyperspectral infrared instrument. In this study, an improved cloud detection scheme using brightness temperature observations from paired HIRAS long-wave infrared (LWIR) and short-wave infrared (SWIR) channels at CO2 absorption bands (15-μm and 4.3-μm) is developed. The weighting function broadness and a set of height-dependent thresholds of cloud-sensitive-level differences are incorporated into pairing LWIR and SWIR channels. HIRAS brightness temperature observations made under clear-sky conditions during a training period are used to develop a set of linear regression equations between paired LWIR and SWIR channels. Moderate-resolution Imaging Spectroradiometer (MODIS) cloud mask data are used for selecting HIRAS clear-sky observations. Cloud Emission and Scattering Indices (CESIs) are defined as the differences in SWIR channels between HIRAS observations and regression simulations from LWIR observations. The cloud retrieval products of ice cloud optical depth and cloud-top pressure from the Atmospheric Infrared Sounder (AIRS) are used to illustrate the effectiveness of the proposed cloud detection scheme for FY-3D HIRAS observations. Results show that the distributions of modified CESIs at different altitudes can capture features in the distributions of AIRS-retrieved ice cloud optical depth and cloud-top pressure better than the CESIs obtained by the original method.

scholarly journals Assessment of cloud-related fine-mode AOD enhancements based on AERONET SDA product

2017 ◽  
Vol 17 (9) ◽  
pp. 5991-6001 ◽  
Author(s):  
Antti Arola ◽  
Thomas F. Eck ◽  
Harri Kokkola ◽  
Mikko R. A. Pitkänen ◽  
Sami Romakkaniemi
Keyword(s):  
Optical Depth ◽  
Relative Role ◽  
Hygroscopic Growth ◽  
Cloud Optical Depth ◽  
Cloud Processing ◽  
Clear Sky ◽  
Coarse Mode ◽  
Fine Mode ◽  
L1 And L2 ◽  
Sky Conditions

Abstract. AERONET (AErosol RObotic NETwork), which is a network of ground-based sun photometers, produces a data product called the aerosol spectral deconvolution algorithm (SDA) that utilizes spectral total aerosol optical depth (AOD) data to infer the component fine- and coarse-mode optical depths at 500 nm. Based on its assumptions, SDA identifies cloud optical depth as the coarse-mode AOD component and therefore effectively computes the fine-mode AOD also in mixed cloud–aerosol observations. Therefore, it can be argued that the more representative AOD for fine-mode fraction should be based on all direct sun measurements and not only on those cloud screened for clear-sky conditions, i.e., on those from level 1 (L1) instead of level 2 (L2) in AERONET. The objective of our study was to assess, including all the available AERONET sites, how the fine-mode AOD is enhanced in cloudy conditions, contrasting SDA L1 and L2 in our analysis. Assuming that the cloud screening correctly separates the cloudy and clear-sky conditions, then the increases in fine-mode AOD can be due to various cloud-related processes, mainly by the strong hygroscopic growth of particles in the vicinity of clouds and in-cloud processing leading to growth of accumulation mode particles. We estimated these cloud-related enhancements in fine-mode AOD seasonally and found, for instance, that in June–August season the average over all the AERONET sites was 0.011, when total fine-mode AOD from L2 data was 0.154; therefore, the relative enhancement was 7 %. The enhancements were largest, both absolutely and relatively, in East Asia; for example, in June–August season the absolute and relative differences in fine-mode AOD, between L1 and L2 measurements, were 0.022 and 10 %, respectively. Corresponding values in North America and Europe were about 0.01 and 6–7 %. In some highly polluted areas, the enhancement is greater than these regional averages, e.g., in Beijing region and in June–July–August (JJA) season the corresponding absolute values were about 0.1. It is difficult to separate the fine-mode AOD enhancements due to in-cloud processing and hygroscopic growth, but we attempted to get some understanding by conducting a similar analysis for SDA-based fine-mode Ångström exponent (AE) patterns. Moreover, we exploited a cloud parcel model, in order to understand in detail the relative role of different processes. We found that in marine conditions, were aerosol concentration are low and cloud scavenging is efficient, the AE changes in opposite direction than in the more polluted conditions, were hygroscopic growth of particles leads to a negative AE change.

scholarly journals Experimental and modeled UV erythemal irradiance under overcast conditions: the role of cloud optical depth

2012 ◽  
Vol 12 (8) ◽  
pp. 21241-21266
Author(s):  
M. Antón ◽  
L. Alados-Arboledas ◽  
J. L. Guerrero-Rascado ◽  
M. J. Costa ◽  
J. C. Chiu ◽  
...  
Keyword(s):  
Optical Depth ◽  
Relative Difference ◽  
Visible Range ◽  
Exponential Dependence ◽  
Cloud Optical Depth ◽  
Clear Sky ◽  
Modification Factor ◽  
High Bias ◽  
Sky Conditions

Abstract. This paper evaluates the relationship between the cloud modification factor (CMF) in the ultraviolet erythemal range and the cloud optical depth (COD) retrieved from the Aerosol Robotic Network (AERONET) "cloud mode" algorithm under overcast cloudy conditions (confirmed with sky images) at Granada (Spain). Empirical CMF showed a clear exponential dependence on experimental COD values, decreasing approximately from 0.7 for COD = 10 to 0.25 for COD = 50. In addition, these COD measurements were used as input in the LibRadtran radiative transfer code allowing the simulation of CMF values for the selected overcast cases. The modeled CMF exhibited a dependence on COD similar to the empirical CMF, but modeled values present a strong underestimation with respect to the empirical factors (mean bias of 22%). To explain this high bias, an exhaustive comparison between modeled and experimental UV erythemal irradiance (UVER) data was performed. This exercise revealed that a significant part of the bias (~8%) may be related to code's overestimation of the experimental data for clear-sky conditions. The rest of the bias (~14%) may be attributed to the substantial underestimation of modeled UVER with respect to experimental UVER under overcast conditions, although the correlation between both dataset was high (R2 ~0.93). A sensitive test showed that the main responsible for that underestimation is the experimental AERONET COD used as input in the simulations, which has been retrieved from zenith radiances in the visible range. In this sense, effective COD in the erythemal interval were derived from an iteration procedure based on searching the best match between modeled and experimental UVER values for each selected overcast case. These effective COD values were smaller than AERONET COD data in about 80% of the overcast cases with a mean relative difference of 22%.

scholarly journals Experimental and modeled UV erythemal irradiance under overcast conditions: the role of cloud optical depth

2012 ◽  
Vol 12 (23) ◽  
pp. 11723-11732 ◽  
Author(s):  
M. Antón ◽  
L. Alados-Arboledas ◽  
J. L. Guerrero-Rascado ◽  
M. J. Costa ◽  
J. C Chiu ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Optical Depth ◽  
Relative Difference ◽  
Visible Range ◽  
Exponential Dependence ◽  
Cloud Optical Depth ◽  
Clear Sky ◽  
Modification Factor ◽  
High Bias ◽  
Sky Conditions

Abstract. This paper evaluates the relationship between the cloud modification factor (CMF) in the ultraviolet erythemal range and the cloud optical depth (COD) retrieved from the Aerosol Robotic Network (AERONET) "cloud mode" algorithm under overcast cloudy conditions (confirmed with sky images) at Granada, Spain, mainly for non-precipitating, overcast and relatively homogenous water clouds. Empirical CMF showed a clear exponential dependence on experimental COD values, decreasing approximately from 0.7 for COD = 10 to 0.25 for COD = 50. In addition, these COD measurements were used as input in the LibRadtran radiative transfer code allowing the simulation of CMF values for the selected overcast cases. The modeled CMF exhibited a dependence on COD similar to the empirical CMF, but modeled values present a strong underestimation with respect to the empirical factors (mean bias of 22%). To explain this high bias, an exhaustive comparison between modeled and experimental UV erythemal irradiance (UVER) data was performed. The comparison revealed that the radiative transfer simulations were 8% higher than the observations for clear-sky conditions. The rest of the bias (~14%) may be attributed to the substantial underestimation of modeled UVER with respect to experimental UVER under overcast conditions, although the correlation between both dataset was high (R2 ~ 0.93). A sensitive test showed that the main reason responsible for that underestimation is the experimental AERONET COD used as input in the simulations, which has been retrieved from zenith radiances in the visible range. In this sense, effective COD in the erythemal interval were derived from an iteration procedure based on searching the best match between modeled and experimental UVER values for each selected overcast case. These effective COD values were smaller than AERONET COD data in about 80% of the overcast cases with a mean relative difference of 22%.

scholarly journals A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm

2018 ◽  
Vol 11 (10) ◽  
pp. 5837-5864 ◽  
Author(s):  
Hiren Jethva ◽  
Omar Torres ◽  
Changwoo Ahn
Keyword(s):  
Atlantic Ocean ◽  
Optical Depth ◽  
Frequency Of Occurrence ◽  
Cloud Optical Depth ◽  
Low Level ◽  
Aerosol Cloud ◽  
Aerosol Absorption ◽  
The World ◽  
Absorbing Aerosols

Abstract. Aerosol–cloud interaction continues to be one of the leading uncertain components of climate models, primarily due to the lack of adequate knowledge of the complex microphysical and radiative processes of the aerosol–cloud system. Situations when light-absorbing aerosols such as carbonaceous particles and windblown dust overlay low-level cloud decks are commonly found in several regions of the world. Contrary to the known cooling effects of these aerosols in cloud-free scenario over darker surfaces, an overlapping situation of the absorbing aerosols over the cloud can lead to a significant level of atmospheric absorption exerting a positive radiative forcing (warming) at the top of the atmosphere. We contribute to this topic by introducing a new global product of above-cloud aerosol optical depth (ACAOD) of absorbing aerosols retrieved from the near-UV observations made by the Ozone Monitoring Instrument (OMI) onboard NASA's Aura platform. Physically based on an unambiguous “color ratio” effect in the near-UV caused by the aerosol absorption above the cloud, the OMACA (OMI above-cloud aerosols) algorithm simultaneously retrieves the optical depths of aerosols and clouds under a prescribed state of the atmosphere. The OMACA algorithm shares many similarities with the two-channel cloud-free OMAERUV algorithm, including the use of AIRS carbon monoxide for aerosol type identification, CALIOP-based aerosol layer height dataset, and an OMI-based surface albedo database. We present the algorithm architecture, inversion procedure, retrieval quality flags, initial validation results, and results from a 12-year long OMI record (2005–2016) including global climatology of the frequency of occurrence, ACAOD, and aerosol-corrected cloud optical depth. A comparative analysis of the OMACA-retrieved ACAOD, collocated with equivalent accurate measurements from the HSRL-2 lidar for the ORACLES Phase I operation (August–September 2016), revealed a good agreement (R = 0.77, RMSE = 0.10). The long-term OMACA record reveals several important regions of the world, where the carbonaceous aerosols from the seasonal biomass burning and mineral dust originated over the continents are found to overlie low-level cloud decks with moderate (0.3 < ACAOD < 0.5, away from the sources) to higher levels of ACAOD (> 0.8 in the proximity to the sources), including the southeastern Atlantic Ocean, southern Indian Ocean, Southeast Asia, the tropical Atlantic Ocean off the coast of western Africa, and northern Arabian sea. No significant long-term trend in the frequency of occurrence of aerosols above the clouds and ACAOD is noticed when OMI observations that are free from the “row anomaly” throughout the operation are considered. If not accounted for, the effects of aerosol absorption above the clouds introduce low bias in the retrieval of cloud optical depth with a profound impact on increasing ACAOD and cloud brightness. The OMACA aerosol product from OMI presented in this paper offers a crucial missing piece of information from the aerosol loading above cloud that will help us to quantify the radiative effects of clouds when overlaid with aerosols and their resultant impact on cloud properties and climate.

scholarly journals Spatiotemporal climate and vegetation greenness changes and their nexus for Dhidhessa River Basin, Ethiopia

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Gizachew Kabite Wedajo ◽  
Misgana K. Muleta ◽  
Berhan Gessesse ◽  
Sifan A. Koriche
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
River Basin ◽  
Climate Change Adaptation ◽  
Spatial Relationship ◽  
Local Scale ◽  
Climate Variables ◽  
Climatic Zones ◽  
Vegetation Greenness ◽  
Climate Change Adaptation Strategies

Abstract Background Understanding spatiotemporal climate and vegetation changes and their nexus is key for designing climate change adaptation strategies at a local scale. However, such a study is lacking in many basins of Ethiopia. The objectives of this study were (i) to analyze temperature, rainfall and vegetation greenness trends and (ii) determine the spatial relationship of climate variables and vegetation greenness, characterized using Normalized Difference in Vegetation Index (NDVI), for the Dhidhessa River Basin (DRB). Quality checked high spatial resolution satellite datasets were used for the study. Mann–Kendall test and Sen’s slope method were used for the trend analysis. The spatial relationship between climate change and NDVI was analyzed using geographically weighted regression (GWR) technique. Results According to the study, past and future climate trend analysis generally showed wetting and warming for the DRB where the degree of trends varies for the different time and spatial scales. A seasonal shift in rainfall was also observed for the basin. These findings informed that there will be a negative impact on rain-fed agriculture and water availability in the basin. Besides, NDVI trends analysis generally showed greening for most climatic zones for the annual and main rainy season timescales. However, no NDVI trends were observed in all timescales for cool sub-humid, tepid humid and warm humid climatic zones. The increasing NDVI trends could be attributed to agroforestry practices but do not necessarily indicate improved forest coverage for the basin. The change in NDVI was positively correlated to rainfall (r2 = 0.62) and negatively correlated to the minimum (r2 = 0.58) and maximum (r2 = 0.45) temperature. The study revealed a strong interaction between the climate variables and vegetation greenness for the basin that further influences the biophysical processes of the land surface like the hydrologic responses of a basin. Conclusion The study concluded that the trend in climate and vegetation greenness varies spatiotemporally for the DRB. Besides, the climate change and its strong relationship with vegetation greenness observed in this study will further affect the biophysical and environmental processes in the study area; mostly negatively on agricultural and water resource sectors. Thus, this study provides helpful information to device climate change adaptation strategies at a local scale.

scholarly journals Climate Change Patterns of Wild Blueberry Fields in Downeast, Maine over the Past 40 Years

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 594
Author(s):  
Rafa Tasnim ◽  
Francis Drummond ◽  
Yong-Jiang Zhang
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Potential Evapotranspiration ◽  
Growing Season ◽  
Spatial Average ◽  
Climate Variables ◽  
Threshold Values ◽  
The Past ◽  
Wild Blueberry ◽  
Crop Health

Maine, USA is the largest producer of wild blueberries (Vaccinium angustifolium Aiton), an important native North American fruit crop. Blueberry fields are mainly distributed in coastal glacial outwash plains which might not experience the same climate change patterns as the whole region. It is important to analyze the climate change patterns of wild blueberry fields and determine how they affect crop health so fields can be managed more efficiently under climate change. Trends in the maximum (Tmax), minimum (Tmin) and average (Tavg) temperatures, total precipitation (Ptotal), and potential evapotranspiration (PET) were evaluated for 26 wild blueberry fields in Downeast Maine during the growing season (May–September) over the past 40 years. The effects of these climate variables on the Maximum Enhanced Vegetation Index (EVImax) were evaluated using Remote Sensing products and Geographic Information System (GIS) tools. We found differences in the increase in growing season Tmax, Tmin, Tavg, and Ptotal between those fields and the overall spatial average for the region (state of Maine), as well as among the blueberry fields. The maximum, minimum, and average temperatures of the studied 26 wild blueberry fields in Downeast, Maine showed higher rates of increase than those of the entire region during the last 40 years. Fields closer to the coast showed higher rates of warming compared with the fields more distant from the coast. Consequently, PET has been also increasing in wild blueberry fields, with those at higher elevations showing lower increasing rates. Optimum climatic conditions (threshold values) during the growing season were explored based on observed significant quadratic relationships between the climate variables (Tmax and Ptotal), PET, and EVImax for those fields. An optimum Tmax and PET for EVImax at 22.4 °C and 145 mm/month suggest potential negative effects of further warming and increasing PET on crop health and productivity. These climate change patterns and associated physiological relationships, as well as threshold values, could provide important information for the planning and development of optimal management techniques for wild blueberry fields experiencing climate change.

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