Suchergebnisse

Abstract. Recurrent extreme landscape fire episodes associated with
drought events in Indonesia pose severe environmental, societal and economic
threats. The ability to predict severe fire episodes months in advance would
enable relevant agencies and communities to more effectively initiate fire-preventative measures and mitigate fire impacts. While dynamic seasonal
climate predictions are increasingly skilful at predicting fire-favourable
conditions months in advance in Indonesia, there is little evidence that
such information is widely used yet by decision makers. In this study, we move beyond forecasting fire risk based on drought
predictions at seasonal timescales and (i) develop a probabilistic early
fire warning system for Indonesia (ProbFire) based on a multilayer perceptron
model using ECMWF SEAS5 (fifth-generation seasonal forecasting system) dynamic climate forecasts together with forest
cover, peatland extent and active-fire datasets that can be operated on a
standard computer; (ii) benchmark the performance of this new system for the
2002–2019 period; and (iii) evaluate the potential economic benefit of such integrated forecasts for Indonesia. ProbFire's event probability predictions outperformed climatology-only based
fire predictions at 2- to 4-month lead times in south Kalimantan, south
Sumatra and south Papua. In central Sumatra, an improvement was observed
only at a 0-month lead time, while in west Kalimantan seasonal predictions did
not offer any additional benefit over climatology-only-based predictions. We
(i) find that seasonal climate forecasts coupled with the fire probability
prediction model confer substantial benefits to a wide range of stakeholders
involved in fire management in Indonesia and (ii) provide a blueprint for
future operational fire warning systems that integrate climate predictions
with non-climate features.

Abstract. Large-scale fires occur frequently across Indonesia, particularly in the southern region of Kalimantan and eastern Sumatra. They have considerable impacts on carbon emissions, haze production, biodiversity, health, and economic activities. In this study, we demonstrate that severe fire and haze events in Indonesia can generally be predicted months in advance using predictions of seasonal rainfall from the ECMWF System 4 coupled ocean–atmosphere model. Based on analyses of long, up-to-date series observations on burnt area, rainfall, and tree cover, we demonstrate that fire activity is negatively correlated with rainfall and is positively associated with deforestation in Indonesia. There is a contrast between the southern region of Kalimantan (high fire activity, high tree cover loss, and strong non-linear correlation between observed rainfall and fire) and the central region of Kalimantan (low fire activity, low tree cover loss, and weak, non-linear correlation between observed rainfall and fire). The ECMWF seasonal forecast provides skilled forecasts of burnt and fire-affected area with several months lead time explaining at least 70% of the variance between rainfall and burnt and fire-affected area. Results are strongly influenced by El Niño years which show a consistent positive bias. Overall, our findings point to a high potential for using a more physical-based method for predicting fires with several months lead time in the tropics rather than one based on indexes only. We argue that seasonal precipitation forecasts should be central to Indonesia's evolving fire management policy.

Abstract. The Canadian Forest Fire Weather Index (FWI) System is the mostly widely used fire danger rating system in the world. We have developed a global database of daily FWI System calculations, beginning in 1980, called the Global Fire WEather Database (GFWED) gridded to a spatial resolution of 0.5° latitude by 2/3° longitude. Input weather data were obtained from the NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and two different estimates of daily precipitation from rain gauges over land. FWI System Drought Code calculations from the gridded data sets were compared to calculations from individual weather station data for a representative set of 48 stations in North, Central and South America, Europe, Russia, Southeast Asia and Australia. Agreement between gridded calculations and the station-based calculations tended to be most different at low latitudes for strictly MERRA-based calculations. Strong biases could be seen in either direction: MERRA DC over the Mato Grosso in Brazil reached unrealistically high values exceeding DC = 1500 during the dry season but was too low over Southeast Asia during the dry season. These biases are consistent with those previously identified in MERRA's precipitation, and they reinforce the need to consider alternative sources of precipitation data. GFWED can be used for analyzing historical relationships between fire weather and fire activity at continental and global scales, in identifying large-scale atmosphere–ocean controls on fire weather, and calibration of FWI-based fire prediction models.

This is the author accepted manuscript. The final version is available from EGU via the DOI in this record. ; The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model. ; S. Rabin was supported by a National Science Foundation Graduate Research Fellowship and by the Carbon Mitigation Initiative, and along with S. Hantson and A. Arneth would like to acknowledge support by the EU FP7 projects BACCHUS (grant agreement no. 603445) and LUC4C (grant agreement no. 603542). This work was supported, in part, by the German Federal Ministry of Education and Research (BMBF), through the Helmholtz Association and its research programme ATMO, and the HGF Impulse and Networking 5 fund. F. Li was funded by the National Natural Science Foundation of China under Grant No. 41475099 and the CAS Youth Innovation Promotion Association Fellowship. The UK Met Office contribution was funded by BEIS under the Hadley Centre Climate Programme contract (GA01101). G. A. Folberth also wishes to acknowledge funding received from the European Union's Horizon 2020 research and innovation programme under grant agreement No 641816 (CRESCENDO). J. O. Kaplan was supported by the European Research Council (COEVOLVE, 313797).

The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copy-right work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce, or limit each other.

REanalysis of the TROpospheric chemical composition over thepast 40 years (RETRO)Objectives:• exploit (often under-utilised) existing data sets from ground based stations,aircraft, and satellite instruments, integrating these into common datasets,• develop tools for the analysis, interpretation and exploitation of the data,• formulate recommendations for future measurement strategies,• assess changes in trace compound emissions and their effect on troposphericchemical composition and aerosols, and the associated radiative forcing, overthe past 40 years,• provide an assessment of uncertainties caused by climate variability,• evaluate emission control strategies in Europe,• predict changes over the next 20 years in tropospheric composition, andradiative forcing through model studies using the emission scenarios definedfor the IPCC 2001 climate assessment,• analyze the magnitude of intercontinental pollutant transport.Scientific achievements:• first detailed, comprehensive and consistent data sets on global emissionsfrom fossil and biofuel combustion and from open vegetation burningcovering the time period 1960-2000; available as gridded data sets with0.5°×0.5° and monthly mean resolution,• first global long-term atmospheric chemistry integrations with several stateof-the-art models using the ERA-40 meteorological data, the RETROemissions and other constrains in a consistent and well-documented manner,• analysis of key parameters controlling the interannual and seasonalvariability and the longer-term trends in the tropospheric composition relatedto ozone and its precursors,• development of new software tools for the analysis of observational data andmodel results; standardisation of model output and data formats anddefinition of model evaluation metrics and skill scores,• development of a comprehensive data base for tropospheric compositionobservations with complete metadata definition and a user-friendly interfacefor data access,• multi-model analysis of specific scenarios related to power generation andthe ...