The Fire Weather Index (FWI) is widely used to assess the meteorological fire danger in several ecosystems worldwide. One shortcoming of the FWI is that only surface weather conditions are considered, despite the important role often played by atmospheric instability in the development of very large wildfires. In this work, we focus on the Iberian Peninsula for the period spanning 2004–2018. We show that atmospheric instability, assessed by the Continuous Haines Index (CHI), can be used to improve estimates of the probability of exceedance of energy released by fires. To achieve this, we consider a Generalized Pareto (GP) model and we show that by stepwisely introducing the FWI and then the CHI as covariates of the GP parameters, the model is improved at each stage. A comprehensive comparison of results using the GP with the FWI as a covariate and the GP with both the FWI and CHI as covariates allowed us to then define a correction to the FWI, dependent on the CHI, that we coined enhanced FWI (FWIe). Besides ensuring a better performance of this improved FWI version, it is important to stress that the proposed FWIe incorporates efficiently the effect of atmospheric instability into an estimation of fire weather danger and can be easily incorporated into existing systems.
North Atlantic Tropical Cyclones (TCs) are major atmospheric hazards that can cause large disruptions to coastal and near-coastal societies. Although most studies focus on those areas with highest impact (e.g., Caribbean Islands, the Gulf and western coast of United States), there is an increasing interest in characterizing changes in occurrence and impacts in areas usually less affected by TCs, particularly in the framework of a changing climate. Here we provide a long-term context evaluating changes in the frequency of TC in the Northeast Atlantic (NEA) basin during the 1978–2019 period. In the last decades, scattered information has shown an impact both from TCs and Post-Tropical Cyclones (PTC) in the NEA. We compute several complementary linear trends and show a significant (p ≤ 0.1) increase in the number of stronger storms in the entire North Atlantic basin, and the amount of TCs and PTCs that reach the NEA, in agreement with previous works. A highly significant relation (p ≤ 0.05) is found between the Atlantic Multidecadal Oscillation (AMO) index and TC activity in both the entire North Atlantic and the NEA basin. Sea surface temperature anomaly maps are produced to better encapsulate the annual variability without the multidecadal oscillation effects and, important cold (warm) pools in cyclogenesis and development zones are found in years with low (high) TC activity. It is also found that the sea surface temperature field plays a minor role in the guiding of storms into the NEA sector. Long-term trends as well as high/low seasonal activity analysis suggest that atmospheric circulation (vertical wind shear, lapse rate, mean sea level pressure and upper-level steering) is more relevant than sea surface temperature in the NEA region.
We describe a methodology to discriminate burned areas and date burning events that use a burn-sensitive (V, W) index system defined in near-/mid-infrared space. Discrimination of burned areas relies on a monthly composite of minimum of W and on the difference between this composite and that of the previous month. The rationale is to identify pixels with high confidence of having burned and aggregate new burned pixels on a contextual basis. Dating of burning events is based on the analysis of time series of W, and searching for the day before maximum temporal separability is achieved. The procedure is applied to the fire of Monchique, a large event that took place in the southwest of Portugal in August 2018. When the obtained pattern of burned pixels is compared against a reference map, the overall accuracy is larger than 99%; the commission and omission errors are lower than 5 and 10%, respectively; and the bias and the Dice coefficient are above 0.95 and 0.9, respectively. Differences between estimated dates of burning and reference dates derived from remote-sensed observations of active fires show a bias of 0.03 day and a root mean square difference of 0.24 day.
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Abstract. Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This urgently demands for accurate urban climate projections to help organizations and individuals to make climate-smart decisions. However, most of the large ensembles of global and regional climate model simulations do not include sophisticated urban parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation reanalysis from the European Centre for Medium-Range Weather Forecasts) and in a simulation with the SURFEX (Surface Externalisée) land surface model employing the widely used bulk bare rock approach. The city of Paris is considered as a case study. Subsequently, we apply a more complex urban scheme – SURFEX coupled to the Town Energy Balance (TEB) urban canopy model to assess its benefits on characterizing the Paris urban climate. Both simulations and ERA5 were compared to the LSA SAF (Satellite Application Facility on Land Surface Analysis) land surface temperature product to evaluate the simulation of Parisian surface urban heat island (SUHI). Our results show a significant added value of SURFEX-TEB in reproducing the SUHI during the daytime and the UHI during both the daytime and nighttime (with overall reductions in the bias and root mean square error and improvements in the representation of the statistics of the SUHI/UHI displayed by the Perkins skill score or S score). The improvement in the simulated SUHI is lower during the nighttime due to the lack of land–atmosphere feedbacks in the proposed offline framework. Nonetheless, the offline SURFEX-TEB framework applied here clearly demonstrates the added value of using more comprehensive parameterization schemes to simulate the urban climate and, therefore, allowing the improvement of urban climate projections.
Abstract. Cities concentrate people, wealth, emissions, and infrastructures, thus representing a challenge and an opportunity for climate change mitigation and adaptation. This places an urgent demand for accurate urban climate projections to help organizations and individuals making climate smart-decisions. However, most of the state-of-the-art global and regional climate models have an oversimplified representation of (or completely neglect) urban climate processes. Here, we use the city of Paris as a case study to show that this is the case for the fifth (and latest) generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and for simulations employing the widely used bulk bare rock approach to urban climate parameterization. Subsequently, we leveraged on the hourly resolution of ERA5 and the Satellite Application Facility Land Surface Analysis (LSA-SAF) land surface temperature product to demonstrate the significant added value of employing the SURFEX land-surface model coupled to Town Energy Balance (TEB) urban canopy model in simulating the Parisian Surface Urban Heat Island (SUHI) during daytime and the urban heat island during both daytime and nighttime. Our results showed the significant added value of SURFEX-TEB in reproducing the observed daytime and nighttime Parisian urban heat island effect. An annual average bias magnitude reduction of 0.5 °C was observed for daytime and around 1.5 °C for nighttime when compared to ERA5 and bare rock approach. Also, SURFEX-TEB revealed an overall better performance in reproducing the observed daytime SUHI, whilst the added value of SURFEX-TEB was lower during nighttime (but still slightly better than ERA5 and the bare rock approach), due to the lack of land-atmosphere feedbacks in the proposed offline framework. Finally, the offline SURFEX-TEB framework applied here demonstrates the ability to simulate the urban climate, which is an asset to build urban climate projections that allow the development of mitigation and adaptation strategies.
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