Stef Lhermitte

Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity (c=0.78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satellite- and meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.

The Impact of the African Great Lakes on the Regional Climate

AbstractAlthough the African Great Lakes are important regulators for the East African climate, their influence on atmospheric dynamics and the regional hydrological cycle remains poorly understood. This study aims to assess this impact by comparing a regional climate model simulation that resolves individual lakes and explicitly computes lake temperatures to a simulation without lakes. The Consortium for Small-Scale Modelling model in climate mode (COSMO-CLM) coupled to the Freshwater Lake model (FLake) and Community Land Model (CLM) is used to dynamically downscale a simulation from the African Coordinated Regional Downscaling Experiment (CORDEX-Africa) to 7-km grid spacing for the period of 1999–2008. Evaluation of the model reveals good performance compared to both in situ and satellite observations, especially for spatiotemporal variability of lake surface temperatures (0.68-K bias), and precipitation (−116 mm yr−1 or 8% bias). Model integrations indicate that the four major African Great Lakes almos...

How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems

Increasing frequency of extreme climate events is likely to impose increased stress on ecosystems and to jeopardize the services that ecosystems provide. Therefore, it is of major importance to assess the effects of extreme climate events on the temporal stability (i.e., the resistance, the resilience, and the variance) of ecosystem properties. Most time series of ecosystem properties are, however, affected by varying data characteristics, uncertainties, and noise, which complicate the comparison of ecosystem stability metrics (ESMs) between locations. Therefore, there is a strong need for a more comprehensive understanding regarding the reliability of stability metrics and how they can be used to compare ecosystem stability globally. The objective of this study was to evaluate the performance of temporal ESMs based on time series of the Moderate Resolution Imaging Spectroradiometer derived Normalized Difference Vegetation Index of 15 global land-cover types. We provide a framework (i) to assess the reliability of ESMs in function of data characteristics, uncertainties and noise and (ii) to integrate reliability estimates in future global ecosystem stability studies against climate disturbances. The performance of our framework was tested through (i) a global ecosystem comparison and (ii) an comparison of ecosystem stability in response to the 2003 drought. The results show the influence of data quality on the accuracy of ecosystem stability. White noise, biased noise, and trends have a stronger effect on the accuracy of stability metrics than the length of the time series, temporal resolution, or amount of missing values. Moreover, we demonstrate the importance of integrating reliability estimates to interpret stability metrics within confidence limits. Based on these confidence limits, other studies dealing with specific ecosystem types or locations can be put into context, and a more reliable assessment of ecosystem stability against environmental disturbances can be obtained.

Performance of atmospheric and topographic correction methods on Landsat imagery in mountain areas

An effective removal of atmospheric and topographic effects on remote-sensing imagery is an essential preprocessing step for mapping land cover accurately in mountain areas. Various techniques that remove these effects have been proposed and consist of specific combinations of an atmospheric and a topographic correction (TC) method. However, it is possible to generate a wide range of new combined correction methods by applying alternative combinations of atmospheric and TC methods. At present, a systematic overview of the statistical performance and data input requirement of preprocessing techniques is missing. In order to assess the individual and combined impacts of atmospheric and TC methods, 15 permutations of two atmospheric and/or four TC methods were evaluated statistically and compared to the uncorrected imagery. Furthermore, results of the integrated ATCOR3 method were included. This evaluation was performed in a study area in the Romanian Carpathian mountains. Results showed that the combination of a transmittance-based atmospheric correction (AC), which corrects the effects of Rayleigh scattering and water-vapour absorption, and a pixel-based C or Minnaert TC, which account for diffuse sky irradiance, reduced the image distortions most efficiently. Overall results indicated that TC had a larger impact than AC and there was a trade-off between the statistical performance of preprocessing techniques and their data requirement. However, the normalized difference vegetation index analysis indicated that atmospheric methods resulted in a larger impact on the spectral information in bands 3 and 4.

Hazardous thunderstorm intensification over Lake Victoria

Weather extremes have harmful impacts on communities around Lake Victoria, where thousands of fishermen die every year because of intense night-time thunderstorms. Yet how these thunderstorms will evolve in a future warmer climate is still unknown. Here we show that Lake Victoria is projected to be a hotspot of future extreme precipitation intensification by using new satellite-based observations, a high-resolution climate projection for the African Great Lakes and coarser-scale ensemble projections. Land precipitation on the previous day exerts a control on night-time occurrence of extremes on the lake by enhancing atmospheric convergence (74%) and moisture availability (26%). The future increase in extremes over Lake Victoria is about twice as large relative to surrounding land under a high-emission scenario, as only over-lake moisture advection is high enough to sustain Clausius–Clapeyron scaling. Our results highlight a major hazard associated with climate change over East Africa and underline the need for high-resolution projections to assess local climate change.

A tipping point in refreezing accelerates mass loss of Greenland’s glaciers and ice caps

Melting of the Greenland ice sheet (GrIS) and its peripheral glaciers and ice caps (GICs) contributes about 43% to contemporary sea level rise. While patterns of GrIS mass loss are well studied, the spatial and temporal evolution of GICs mass loss and the acting processes have remained unclear. Here we use a novel, 1 km surface mass balance product, evaluated against in situ and remote sensing data, to identify 1997 (±5 years) as a tipping point for GICs mass balance. That year marks the onset of a rapid deterioration in the capacity of the GICs firn to refreeze meltwater. Consequently, GICs runoff increases 65% faster than meltwater production, tripling the post-1997 mass loss to 36±16 Gt−1, or ∼14% of the Greenland total. In sharp contrast, the extensive inland firn of the GrIS retains most of its refreezing capacity for now, buffering 22% of the increased meltwater production. This underlines the very different response of the GICs and GrIS to atmospheric warming.

How does the spaceborne radar blind zone affect derived surface snowfall statistics in polar regions

Global statistics of snowfall are currently only available from the CloudSat satellite. But CloudSat cannot provide observations of clouds and precipitation within the so-called blind zone, which is caused by ground-clutter contamination of the CloudSat radar and covers the last 1200 m above land/ice surface. In this study, the impact of the blind zone of CloudSat on derived snowfall statistics in polar regions is investigated by analyzing three 12 month data sets recorded by ground-based Micro Rain Radar (MRR) at the Belgian Princess Elisabeth station in East Antarctica and at Ny-Alesund and Longyearbyen in Svalbard, Norway. MRR radar reflectivity profiles are investigated in respect to vertical variability in the frequency distribution, changes in the number of observed snow events, and impacts on total precipitation. Results show that the blind zone leads to reflectivity being underestimated by up to 1 dB, the number of events being altered by ±5% and the precipitation amount being underestimated by 9 to 11 percentage points. Besides investigating a blind zone of 1200 m, the impacts of a reduced blind zone of 600 m are also analyzed. This analysis will help in assessing future missions with a smaller blind zone. The reduced blind zone leads to improved representation of mean reflectivity but does not improve the bias in event numbers and precipitation amount.

Species-rich semi-natural grasslands have a higher resistance but a lower resilience than intensively managed agricultural grasslands in response to climate anomalies

The stable delivery of ecosystem services provided by grasslands is strongly dependent on the stability of grassland ecosystem functions such as biomass production. Biomass production is in turn strongly affected by the frequency and intensity of climate extremes. The aim of this study is to evaluate to what extent species-poor intensively managed agricultural grasslands can maintain their biomass productivity under climate anomalies, as compared to species-rich, semi-natural grasslands. Our hypothesis is that species richness stabilizes biomass production over time. Biomass production stability was assessed in response to drought and temperature anomalies using 14 years of the Normalized Difference Vegetation Index (NDVI), temperature and drought index time series. More specifically, vegetation resistance (i.e. the ability to withstand the climate anomaly) and resilience (i.e. the recovery rate) were derived using an auto-regressive model with external input variables (ARx). The stability metrics for both grasslands were subsequently compared. We found that semi-natural grasslands exhibited a higher resistance but lower resilience than agricultural grasslands in the Netherlands. Furthermore, the difference in stability between semi-natural and agricultural grasslands was dependent on the physical geography: the most significant differences in resistance were observed in coastal dunes and riverine areas, whereas the differences in resilience were the most significant in coastal dunes and fens. Synthesis and applications. We conclude that semi-natural grasslands show a higher resistance to drought and temperature anomalies compared to agricultural grasslands. These results underline the need to reassess the ways agricultural practices are performed. More specifically, increasing the plant species richness of agricultural grasslands and lowering their mowing and grazing frequency may contribute to buffer their biomass production stability against climate extremes.

Evaluation of the CloudSat surface snowfall product overAntarctica using ground-based precipitation radars

Before addressing the comments of the reviewer, it must be noted that during the revision process there was detected that a small part of erroneous MRR data at the PE station was included in the analysis. This erroneous data was recorded during the 2015-2016 austral winter season and was caused by interference from other instruments. It was removed from the sample lowering the period of concurrent data availability of the MRR and CloudSat from 928 to 851 days for the PE station (Fig. 2 in the main paper). This mainly affects Fig. 6 in the main paper where a clear lowering of both the MRR and CloudSat total precipitation amount is observed. However, as the total snowfall amount for both the MRR and CloudSat lowered with an equal amount, results and conclusions are not affected significantly.

Polar clouds and radiation in satellite observations, reanalyses, and climate models: POLAR CLOUDS AND RADIATION

Clouds play a pivotal role in the surface energy budget of the polar regions. Here we use two largely independent data sets of cloud and surface downwelling radiation observations derived by satellite remote sensing (2007–2010) to evaluate simulated clouds and radiation over both polar ice sheets and oceans in state-of-the-art atmospheric reanalyses (ERA-Interim and Modern Era Retrospective-Analysis for Research and Applications-2) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model ensemble. First, we show that, compared to Clouds and the Earths Radiant Energy System-Energy Balanced and Filled, CloudSat-CALIPSO better represents cloud liquid and ice water path over high latitudes, owing to its recent explicit determination of cloud phase that will be part of its new R05 release. The reanalyses and climate models disagree widely on the amount of cloud liquid and ice in the polar regions. Compared to the observations, we find significant but inconsistent biases in the model simulations of cloud liquid and ice water, as well as in the downwelling radiation components. The CMIP5 models display a wide range of cloud characteristics of the polar regions, especially with regard to cloud liquid water, limiting the representativeness of the multimodel mean. A few CMIP5 models (CNRM, GISS, GFDL, and IPSL_CM5b) clearly outperform the others, which enhances credibility in their projected future cloud and radiation changes over high latitudes. Given the rapid changes in polar regions and global feedbacks involved, future climate model developments should target improved representation of polar clouds. To that end, remote sensing observations are crucial, in spite of large remaining observational uncertainties, which is evidenced by the substantial differences between the two data sets.