This article presents a novel downscaling method based on a geomatic spatial model. This model is constructed on the basis of regressions between topographical (explanatory) variables (digitized here at 200 m resolution) and climatic factors observed at meteorological stations. These regressions are used to calibrate the model in the form of two parameters: the frequency with which explanatory variables are significant at the 95% level, and the mean of the regression coefficient associated with each variable. One of the objectives of the article is to test the relevance of the method and the application made of it in this study bears on daily observed minimum (tn) and maximum (tx) temperatures in the Mont Blanc massif between 1979 and 2014. Estimations show weak statistical biases and the temperature variation from day to day is well represented. The root mean square errors of daily temperatures are of the order of 2 °C for tn and tx alike. The validation shows (1) the value of applying spatial models to two atmospheric configurations (weather regimes and local rainfall conditions) and (2) the limitations of the method. Eventually, this method shall be applied to the downscaling of large‐scale atmospheric parameters provided by earth system models for projecting the temperature of the lower layers of the atmosphere over future decades.
The tropical cloud forest ecosystem in Western Equatorial Africa (WEA) is known to be sensitive to the presence of an extensive and persistent low-level stratiform cloud deck during the long dry season from June to September (JJAS). Here we present a new climatology of the diurnal cycle of the low-level cloud cover from surface synoptic stations over WEA during JJAS 1971–2019. For the period JJAS 2008–2019, we also utilized estimates of cloudiness from four satellite products, namely the Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting (SAFNWC) cloud classification, the Day and Night Microphysical Schemes (DMS/NMS) and cross-sections from CALIPSO and CloudSat (2B-GEOPROF-LIDAR). A comparison with surface stations reveals that the NMS at night together with SAFNWC at daytime yield the smallest biases. The climatological analysis reveals that low-level clouds persist during the day over the coastal plains and windward side of the low mountain ranges. Conversely, on their leeward sides, i.e. over the plateaus, a decrease of the low-level cloud frequency is observed in the afternoon, together with a change from stratocumulus to cumulus. At night, the low-level cloud deck reforms over this region with the largest cloud occurrence frequencies in the morning. Vertical profiles from 2B-GEOPROF-LIDAR reveal cloud tops below 3000 m even at daytime. The station data and the suitable satellite products form the basis to better understand the physical processes controlling the clouds and to evaluated cloudiness from reanalyses and models.
The Arctic region has experienced significant warming during the past two decades with major implications on the cryosphere. The causes of Arctic amplification are still an open question within the scientific community, attracting recent interest. The goal of this study is to quantify the contribution of atmospheric circulation on temperature variability in the Atlantic–Arctic region at decadal to intra‐annual timescales from 1951 to 2014. Daily 20th Century reanalyses geopotential height anomalies at 500 hPa were clustered into different weather regimes to assess their contribution to observed temperature variability. The results show that in winter, 25% of the warming (cooling) in the North Atlantic Ocean (northeastern Canada) is due to temporal decreases of high geopotential anomalies in Greenland. This regime influences air mass migration patterns, bringing less cold (warm) air masses into these regions. Additionally, atmospheric warming or cooling has been attributed to a change in nearby oceanic basin surface conditions because of sea ice decline. In summer, about 15% of the warming observed in Norwegian/Greenland Seas is related to an increase in temporal anticyclonic patterns. This ratio reaches 37% in Norway due to an amplification from downwards solar radiation. This study allows for better understanding how natural climate variability modulates the regional signature of climate change and estimating the uncertainties in climate projections.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.