Abstract:The European Organization for the Exploitation of Meteorological Satellites' (EUMETSAT) Meteosat satellites provide the unique opportunity to compile a 30+ year land surface temperature (LST) climate data record. Since the Meteosat instrument on-board Meteosat 2-7 is equipped with a single thermal channel, single-channel LST retrieval algorithms are used to ensure consistency across Meteosat satellites. The present study compares the performance of two single-channel LST retrieval algorithms: (1) A physical radiative transfer-based mono-window (PMW); and (2) a statistical mono-window model (SMW). The performance of the single-channel algorithms is assessed using a database of synthetic radiances for a wide range of atmospheric profiles and surface variables. The two single-channel algorithms are evaluated against the commonly-used generalized split-window OPEN ACCESSRemote Sens. 2015, 7 13140(GSW) model. The three algorithms are verified against more than 60,000 LST ground observations with dry to very moist atmospheres (total column water vapor (TCWV) 1-56 mm). Except for very moist atmospheres (TCWV > 45 mm), results show that Meteosat single-channel retrievals match those of the GSW algorithm by 0.1-0.5 K. This study also outlines that it is possible to put realistic uncertainties on Meteosat single-channel LSTs, except for very moist atmospheres: simulated theoretical uncertainties are within 0.3-1.0 K of the in situ root mean square differences for TCWV < 45 mm.
Forests influence climate through a myriad of chemical, physical and biological processes and are an essential lever in the efforts to counter climate change. The majority of studies investigating potential climate benefits from forests have focused on forest area changes, while changes to forest management, in particular those affecting species composition, have received much less attention. Using a statistical model based on remote sensing observations over europe, we show that broadleaved tree species locally reduce land surface temperatures in summer compared to needle-leaved species. The summer mean cooling effect related to an increase in broad-leaved tree fraction of 80% is relatively modest (~ 0.3-0.75 K), but is amplified during exceptionally warm periods. The reduction of daily maximum temperatures during the hottest days reaches up to 1.8 K in the Atlantic region and up to 1.5 K in Continental and Mediterranean regions. Hot temperature extremes adversely affect humans and ecosystems and are expected to become more frequent in a future climate. thus, forest management strategies aiming to increase the fraction of broad-leaved species could help to reduce some of the adverse local impacts caused by hot temperature extremes. However, the overall benefits and trade-offs related to an increase in the broad-leaved tree fraction in European forests needs to be further investigated and assessed carefully when adapting forest management strategies. Forests are expected to play an essential role in climate change mitigation as they can generally sequester more carbon than non-forested ecosystems 1-3. In addition, forests affect water and energy fluxes at the earth surface through biogeophysical processes including changes in evapotranspiration, albedo, and surface roughness 4,5. Various observation-based studies have shown that forests, through these biogeophysical processes, either reduce or increase local temperatures depending on location and time of observation 6-9. In contrast to a comparison of forested and non-forested ecosystems, the potential impacts on temperatures of forest management or more generally changes in forest characteristics are less well documented 10-12. Facilitating an increase of the broad-leaved tree fraction (BTF) in forests is a promising management strategy to enhance the provision of ecosystem services and to adapt to climate change 13-16. For example, increasing the BTF can lead to reduced risk of fires, wind throw and bark beetle outbreaks 15,17. However, the potential benefits of broad-leaved trees through their biogeophysical influence on temperature, in particular on extreme temperatures, have not yet been investigated beyond the site-level scale 18 even though changes on extreme temperatures are highly relevant in terms of impacts on humans and ecosystems 19,20. To investigate how an increase in the BTF in Europe would influence local land surface temperature (LST) we linked observed patterns of LST with patterns of the BTF. In contrast to previous studies, we use remote sens...
Algorithms for Land Surface Temperature (LST) retrieval from infrared measurements are usually sensitive to the amount of water vapor present in the atmosphere. The Satellite Application Facilities on Climate Monitoring and Land Surface Analysis (CM SAF and LSA SAF) are currently compiling a 25 year LST Climate data record (CDR), which uses water vapor information from ERA-Int reanalysis. However, its relatively coarse spatial resolution may lead to systematic errors in the humidity profiles with implications in LST, particularly over mountainous areas. The present study compares LST estimated with three different retrieval algorithms: a radiative transfer-based physical mono-window (PMW), a statistical mono-window (SMW), and a generalized split-windows (GSW). The algorithms were tested over the Alpine region using ERA-Int reanalysis data and relied on the finer spatial scale Consortium for Small-Scale Modelling (COSMO) model data as a reference. Two methods were developed to correct ERA-Int water vapor misestimation: (1) an exponential parametrization of total precipitable water (TPW) appropriate for SMW/GSW; and (2) a level reduction method to be used in PMW. When ERA-Int TPW was used, the algorithm missed the right TPW class in 87% of the cases. When the exponential parametrization was used, the missing class rate decreased to 9%, and when the level reduction method was applied, the LST corrections went up to 1.7 K over the study region. Overall, the correction for pixel orography in TPW leads to corrections in LST estimations, which are relevant to ensure that long-term LST records meet climate requirements, particularly over mountainous regions.
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