A Global Probability‐Of‐Fire (PoF) Forecast McNorton, J. R.; Di Giuseppe, F.; Pinnington, E. ...
Geophysical research letters,
28 June 2024, Letnik:
51, Številka:
12
Journal Article
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Accurate wildfire forecasting can inform regional management and mitigation strategies in advance of fire occurrence. Existing systems typically use fire danger indices to predict landscape ...flammability, based on meteorological forecasts alone, often using little or no direct information on land surface or vegetation state. Here, we use a vegetation characteristic model, weather forecasts and a data‐driven machine learning approach to construct a global daily ∼9 km resolution Probability of Fire (PoF) model operating at multiple lead times. The PoF model outperforms existing indices, providing accurate forecasts of fire activity up to 10 days in advance, and in some cases up to 30 days. The model can also be used to investigate historical shifts in regional fire patterns. Furthermore, the underlying data driven approach allows PoF to be used for fire attribution, isolating key variables for specific fire events or for looking at the relationships between variables and fire occurrence.
Plain Language Summary
Wildfires have widespread effects on local ecosystems, communities, air quality, and global atmospheric conditions. Accurate wildfire forecasts can be used by local communities and agencies to manage and respond to wildfires effectively. As such, it is essential these predictions are not only accurate but are accessible in real‐time and provide sufficient advanced notice to ensure successful actions can be taken. To achieve this we have developed a forecasting system that combines satellite observations, weather forecasting, and vegetation characteristics using machine learning. Tested on historical data and operated in real‐time, our model provides a global daily wildfire forecast with a 9 km resolution, predicting the likelihood of fires up to 30 days in advance. The model outperforms existing fire danger forecasts when evaluated against satellite observations of active fires. It can also identify key drivers which result in the occurrence of fire. Finally, it not only offers real‐time forecasts but can be used to help investigate past fire events, understand their causes, and predict wildfire activity over longer climate timescales.
Key Points
A data‐driven model informed by satellite observations and an Earth System Model provides accurate fire forecasts upto 10 days in advance
The probability of fire forecast is implemented operationally in a numerical weather prediction model to provide real‐time forecasts
Fire attribution is demonstrated and can be used for specific fire events or historical analysis
Atmospheric methane (CH4) accounts for ~20% of the total direct anthropogenic radiative forcing by long‐lived greenhouse gases. Surface observations show a pause (1999–2006) followed by a resumption ...in CH4 growth, which remain largely unexplained. Using a land surface model, we estimate wetland CH4 emissions from 1993 to 2014 and study the regional contributions to changes in atmospheric CH4. Atmospheric model simulations using these emissions, together with other sources, compare well with surface and satellite CH4 data. Modeled global wetland emissions vary by ±3%/yr (σ = 4.8 Tg), mainly due to precipitation‐induced changes in wetland area, but the integrated effect makes only a small contribution to the pause in CH4 growth from 1999 to 2006. Increasing temperature, which increases wetland area, drives a long‐term trend in wetland CH4 emissions of +0.2%/yr (1999 to 2014). The increased growth post‐2006 was partly caused by increased wetland emissions (+3%), mainly from Tropical Asia, Southern Africa, and Australia.
Plain Language Summary
Methane is the second most important greenhouse gas and its atmospheric concentration is increasing. Currently the reasons for this are poorly understood, with several suspected causes. The main single source of methane to the atmosphere is from microbial activity in wetlands. Using computer models this study shows an increase in emissions from wetlands from 1993–2014, mainly as a result of warming temperatures. This could continue into the future with rising temperatures. The increased emissions were found to be partly responsible for the upward trend in atmospheric methane. The study uses satellite atmospheric methane data to help validate the findings.The growth rate of methane slowed down noticeably for a few years at the turn of the 21st century before resuming again in 2007; previous studies concluded this was likely due to changes in wetland emissions. This study finds that while wetland emissions did decrease for some of the years when the methane atmospheric growth rate stalled, they alone could not explain the entire slowdown in growth. Finally, the study finds increased wetland emissions contributed to some of the growth since 2007.
Key Points
An updated land surface model predicts a trend of +0.2%/yr in global wetland CH4 emissions over 1993‐2014, mainly driven by temperature
An atmospheric model using these wetland fluxes, along with other emission estimates, agrees well with ground‐based and satellite CH4 data
Varying global wetland CH4 emissions (±3%) made a small contribution to the 1999‐2006 pause but contributed ~1 ppb/yr to growth post‐2006
The societal benefits of numerical weather prediction (NWP) forecasts are most evident in populated areas. An urban representation within NWP models should provide improved forecast accuracy. Here, ...we present the preliminary implementation of an urban scheme within the Integrated Forecasting System (IFS) using a simplified single‐layer urban canopy model. The scheme makes assumptions of canyon geometry and considers fluxes from roads, walls, and roofs. Temperature observations were used to optimize single‐column model (SCM) parameters using the Gauss‐Newton method. Observation comparisons over six European cities, show a 2‐m temperature root‐mean‐squared error reduction from 1.85 to 1.75 K with the urban scheme. Optimized parameters were used globally at kilometric scale in a land surface model. A sensitivity experiment assuming a 100% urban world showed spatially averaged northern hemisphere 2‐m temperatures increased by 0.54 K (January) and 0.42 K (July) at night caused by changes in the albedo, emissivity, roughness, and thermal and hydrological properties. Global ∼1‐km resolution simulations using ancillary urban mapping information produce an urban heat island effect over major and minor conurbations. Only major conurbations were well represented at ∼9‐km resolution. Results from SCM simulations show a heightening of the planetary boundary layer over city sites, with the largest enhancements occurring at night in July (84 ± 48 m) caused by an increased sensible heat flux. These initial developments show the importance of a high‐resolution urban representation within NWP models. Improved parameterization and mapping will enable an online representation of energy, water, and trace gas fluxes over residential areas.
Plain Language Summary
Urban areas make up only a small fraction of the Earth's surface; however, they are home to over 50% of the world's population. In these areas a phenomenon known as the urban heat island effect causes increased temperatures due to human activities, an effect often missing in weather forecasts. Forecasts, generated using computer models, consider not only the atmosphere but also the role of the land surface on the weather above. Typically these models do not include an urban map, so they miss key urban processes. We introduced a representation of urban areas to the model of the European Center for Medium‐Range Weather Forecasts. We considered several ways in which the urban environment interacts with the weather, including through changes in heat storage and treatment of rainfall. We find these developments result in a more accurate model forecast over six European cities. The model accurately predicts the increased heating observed over cities at night and some of the observed changes in the atmosphere. Future work should continue to improve the urban representation in weather and air quality/greenhouse gas models by implementing an urban scheme in operational forecasts.
Key Points
An urban scheme has been introduced and optimized within the ECMWF IFS single‐column and surface‐only model
Assuming an urban world, average nighttime 2‐m temperatures increased for January (0.54 K) and July (0.42 K) in a surface only simulation
Using realistic urban cover for eight cities, PBL height in July increases by an average of 66 and 84 m for the day and night, respectively
Effective wildfire management and prevention strategies depend on accurate forecasts of fire occurrence and propagation. Fuel load and fuel moisture content are essential variables for forecasting ...fire occurrence, and whilst existing operational systems incorporate dead fuel moisture content, both live fuel moisture content and fuel load are either approximated or neglected. We propose a mid-complexity model combining data driven and analytical methods to predict fuel characteristics. The model can be integrated into earth system models to provide real-time forecasts and climate records taking advantage of meteorological variables, land surface modelling, and satellite observations. Fuel load and moisture is partitioned into live and dead fuels, including both wood and foliage components. As an example, we have generated a 10-year dataset which is well correlated with independent data and largely explains observed fire activity globally. While dead fuel moisture correlates highest with fire activity, live fuel moisture and load are shown to potentially enhance prediction skill. The use of observation data to inform a dynamical model is a crucial first step toward disentangling the contributing factors of fuel and weather to understand fire evolution globally. This dataset, with high spatiotemporal resolution (∼9 km, daily), is the first of its kind and will be regularly updated.
The Pantanal region of Brazil is the largest seasonally flooded tropical grassland and, according to local chamber measurements, a substantial CH4 source. CH4 emissions from wetlands have recently ...become of heightened interest because global atmospheric 13CH4 data indicate they may contribute to the resumption of atmospheric CH4 growth since 2007. We have regularly measured vertical atmospheric profiles for 2 years in the center of the Pantanal with the objectives to obtain an estimate of CH4 emissions using an atmospheric approach, and provide information about flux seasonality and its relation to controlling factors. Boundary layer‐free troposphere differences observed in the Pantanal are large compared to other wetlands. Total emissions based on a planetary boundary layer budgeting technique are 2.0–2.8 TgCH4 yr−1 (maximum flux ∼0.4 gCH4 m−2 d−1) while those based on a Bayesian inversion using an atmospheric transport model are ∼3.3 TgCH4 yr−1. Compared to recent estimates for Amazonia (∼41 ± 3 TgCH4 yr−1, maximum flux ∼0.3 gCH4 m−2 d−1) these emissions are not that large. Our Pantanal data suggest a clear flux seasonality with CH4 being released in large amounts just after water levels begin to rise again after minimum levels have been reached. CH4 emissions decline substantially once the maximum water level has been reached. While predictions with prognostic wetland CH4 emission models agree well with the magnitude of the fluxes, they disagree with the phasing. Our approach shows promise for detecting and understanding longer‐term trends in CH4 emissions and the potential for future wetlands CH4 emissions climate feedbacks.
Plain Language Summary
CH4 emissions contribute substantially to greenhouse warming and atmospheric concentrations continue to grow rapidly. Increases in emissions from wetlands may contribute. We have measured regularly vertical CH4 concentration profiles over the Pantanal, the largest tropical seasonally flooded grasslands, to provide an estimate of these emissions and to determine seasonal cycle. Our estimates are similar to earlier estimates based on direct flux measurements on the ground. Fluxes vary strongly seasonally. They are largest during the rise of water levels and decrease before maximum levels have been reached. Our data show that longer‐term vertical profile measurements could provide an answer whether wetland emissions are changing.
Key Points
Large CH4 boundary layer‐free troposphere differences over Pantanal wetlands revealed by vertical atmospheric CH4 profile data
According to atmospheric data, CH4 flux is large during the early expansion phase of the inundated area and weakens at peak extent
Prognostic global wetlands CH4 emission models have limited skill regarding seasonality of Pantanal emissions
Wetland emissions contribute the largest uncertainties to the current global atmospheric CH.sub.4 budget, and how these emissions will change under future climate scenarios is also still poorly ...understood. Bloom et al. (2017b) developed WetCHARTs, a simple, data-driven, ensemble-based model that produces estimates of CH.sub.4 wetland emissions constrained by observations of precipitation and temperature. This study performs the first detailed global and regional evaluation of the WetCHARTs CH.sub.4 emission model ensemble against 9 years of high-quality, validated atmospheric CH.sub.4 observations from GOSAT (the Greenhouse Gases Observing Satellite). A 3-D chemical transport model is used to estimate atmospheric CH.sub.4 mixing ratios based on the WetCHARTs emissions and other sources.
Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and ...how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model (UKESM) and is capable of generating estimates of wetland methane emissions.In this study, we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 and 0.88 for most regions; however, the seasonal cycle amplitude is typically underestimated (by between 1.8 and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data are found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration outperforms the other, but this does lead to poor performance in some regions.We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1, 19.5 and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the Catchment-based Macro-scale Floodplain (CaMa-Flood) model to explicitly represent river and floodplain water dynamics and find that these JULES-CaMa-Flood simulations are capable of providing a wetland extent that is more consistent with observations in this regions, highlighting this as an important area for future model development.
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