Future emissions from Canadian boreal forest fires

2009 ◽  
Vol 39 (2) ◽  
pp. 383-395 ◽  
Author(s):  
B.D. Amiro ◽  
A. Cantin ◽  
M.D. Flannigan ◽  
W.J. de Groot
Keyword(s):  
Fuel Consumption ◽  
Forest Fires ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Fuel Load ◽  
Weather Index ◽  
Fire Weather Index ◽  
Canadian Boreal Forest ◽  
Almost All

New estimates of greenhouse gas emissions from Canadian forest fires were calculated based on a revised model for fuel consumption, using both the fire fuel load and the Drought Code of the Canadian Forest Fire Weather Index System. This model was applied to future climate scenarios of 2×CO2 and 3×CO2 environments using the Canadian Global Climate Model. Total forest floor fuel consumption for six boreal ecozones was estimated at 60, 80, and 117 Tg dry biomass for the 1×CO2, 2×CO2, and 3×CO2 scenarios, respectively. These ecozones cover the boreal and taiga regions and account for about 86% of the total fire consumption for Canada. Almost all of the increase in fuel consumption for future climates is caused by an increase in the area burned. The effect of more severe fuel consumption density (kilograms of fuel consumed per square metre) is relatively small, ranging from 0% to 18%, depending on the ecozone. The emissions of greenhouse gases from all Canadian fires are estimated to increase from about 162 Tg·year–1 of CO2 equivalent in the 1×CO2 scenario to 313 Tg·year–1 of CO2 equivalent in the 3×CO2 scenario, including contributions from CO2, CH4, and N2O.

Forest floor fuel consumption and carbon emissions in Canadian boreal forest fires

2009 ◽  
Vol 39 (2) ◽  
pp. 367-382 ◽  
Author(s):  
W.J. de Groot ◽  
J.M. Pritchard ◽  
T.J. Lynham
Keyword(s):  
Fuel Consumption ◽  
Carbon Emissions ◽  
Forest Fires ◽  
Forest Floor ◽  
Wildland Fire ◽  
Total Carbon ◽  
Fuel Load ◽  
Fire Weather Index ◽  
Experimental Burn ◽  
Canadian Boreal Forest

In many forest types, over half of the total stand biomass is located in the forest floor. Carbon emissions during wildland fire are directly related to biomass (fuel) consumption. Consumption of forest floor fuel varies widely and is the greatest source of uncertainty in estimating total carbon emissions during fire. We used experimental burn data (59 burns, four fuel types) and wildfire data (69 plots, four fuel types) to develop a model of forest floor fuel consumption and carbon emissions in nonpeatland standing-timber fuel types. The experimental burn and wildfire data sets were analyzed separately and combined by regression to provide fuel consumption models. Model variables differed among fuel types, but preburn fuel load, duff depth, bulk density, and Canadian Forest Fire Weather Index System components at the time of burning were common significant variables. The regression R2 values ranged from 0.206 to 0.980 (P < 0.001). The log–log model for all data combined explained 79.5% of the regression variation and is now being used to estimate annual carbon emissions from wildland fire. Forest floor carbon content at the wildfires ranged from 40.9% to 53.9%, and the carbon emission rate ranged from 0.29 to 2.43 kg·m–2.

scholarly journals Will climate change drive 21st century burn rates in Canadian boreal forest outside of its natural variability: collating global climate model experiments with sedimentary charcoal data

2010 ◽  
Vol 19 (8) ◽  
pp. 1127 ◽  
Author(s):  
Yves Bergeron ◽  
Dominic Cyr ◽  
Martin P. Girardin ◽  
Christopher Carcaillet
Keyword(s):  
Climate Change ◽  
Boreal Forest ◽  
21St Century ◽  
Forest Fires ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Model Experiments ◽  
Clear Cutting ◽  
Canadian Boreal Forest

Natural ecosystems have developed within ranges of conditions that can serve as references for setting conservation targets or assessing the current ecological integrity of managed ecosystems. Because of their climate determinism, forest fires are likely to have consequences that could exacerbate biophysical and socioeconomical vulnerabilities in the context of climate change. We evaluated future trends in fire activity under climate change in the eastern Canadian boreal forest and investigated whether these changes were included in the variability observed during the last 7000 years from sedimentary charcoal records from three lakes. Prediction of future annual area burned was made using simulated Monthly Drought Code data collected from an ensemble of 19 global climate model experiments. The increase in burn rate that is predicted for the end of the 21st century (0.45% year–1 with 95% confidence interval (0.32, 0.59) falls well within the long‐term past variability (0.37 to 0.90% year–1). Although our results suggest that the predicted change in burn rates per se will not move this ecosystem to new conditions, the effects of increasing fire incidence cumulated with current rates of clear‐cutting or other low‐retention types of harvesting, which still prevail in this region, remain preoccupying.

scholarly journals Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Agromet ◽  
2018 ◽  
Vol 32 (1) ◽  
pp. 1
Author(s):  
Laode Nurdiansyah ◽  
Akhmad Faqih
Keyword(s):  
Forest Fires ◽  
Canonical Correlation ◽  
Prediction Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Dry Season ◽  
Global Climate Models ◽  
International Research Institute ◽  
Cluster 2

Predictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aims to predict the rainy and dry season onsets in two cluster regions in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets are developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The model uses Canonical Correlation Analysis (CCA) method available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. The results show that the predictors from HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, the predictors from CWB and POAMA models perform better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of rainy season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2.

scholarly journals Forecasting Season Onsets in Kapuas District Based on Global Climate Model Outputs

Agromet ◽  
2018 ◽  
Vol 32 (1) ◽  
pp. 1
Author(s):  
Laode Nurdiansyah ◽  
Akhmad Faqih
Keyword(s):  
Forest Fires ◽  
Canonical Correlation ◽  
Prediction Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Dry Season ◽  
Global Climate Models ◽  
International Research Institute ◽  
Cluster 2

Predictions of the rainy and dry season onsets are very important in climate risk management processes, especially for the development of early warning system of land and forest fires in Kalimantan. This research aims to predict the rainy and dry season onsets in two cluster regions in Kapuas District, Central Kalimantan. The prediction models used to predict the onsets are developed by using seasonal rainfall data on September-October-November (SON) periods as predicted by five Global Climate Models (GCMs). The model uses Canonical Correlation Analysis (CCA) method available in the Climate Predictability Tool (CPT) software developed by the International Research Institute for Climate and Society (IRI), Columbia University. The results show that the predictors from HMC and POAMA models produce better canonical correlations (r = 0.72 and 0.89, respectively) compared to BCC (r=0.46), CWB (r=0.62), and GDAPS_F (r=0.67) models. In the development of models for predicting the dry season onsets, the predictors from CWB and POAMA models perform better canonical correlation results (r = 0.73 and 0.76, respectively) compared to BCC (r=0.53), GDAPS_F (r=0.64), and HMC (r=0.46) models. In general, the model validations showed that CWB, GDAPS_F, and POAMA models have better predictive skills than BCC and HMC models in predicting onsets of the rainy and dry seasons (with Pearson correlations (r) ranging between 0.30 and 0.75). Experiments on those five models for the predictions of rainy season onset in 2013 showed that the predicted onsets occurred on the range of 8 September to 22 October in Cluster 1 and on 3 to 7 October in Cluster 2. For the predictions of the dry season onsets in 2014, the models predicted the occurrences from 6 to 25 May in Cluster 1 and from 21 to 25 March in Cluster 2.

scholarly journals Forest fire risk assessment in Sweden using climate model data: bias correction and future changes

2015 ◽  
Vol 3 (1) ◽  
pp. 837-890 ◽  
Author(s):  
W. Yang ◽  
M. Gardelin ◽  
J. Olsson ◽  
T. Bosshard
Keyword(s):  
Forest Fire ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Fire Risk ◽  
Climate Modelling ◽  
Post Processing ◽  
Fire Weather Index ◽  
Forest Fire Risk

Abstract. As the risk for a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically downscaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

High bit rate ACTS experiments for performing global science - Keck Telescope and global climate model

1996 ◽  
Author(s):  
Larry Bergman ◽  
J. Gary ◽  
Burt Edelson ◽  
Neil Helm ◽  
Judith Cohen ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Bit Rate ◽  
Global Science ◽  
High Bit Rate

scholarly journals A comparison of ship and satellite measurements of cloud properties with global climate model simulations in the southeast Pacific stratus deck

2010 ◽  
Vol 10 (14) ◽  
pp. 6527-6536 ◽  
Author(s):  
M. A. Brunke ◽  
S. P. de Szoeke ◽  
P. Zuidema ◽  
X. Zeng
Keyword(s):  
Diurnal Cycle ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Cloud Fraction ◽  
Cloud Base ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Southeast Pacific ◽  
Cloud Thickness

Abstract. Here, liquid water path (LWP), cloud fraction, cloud top height, and cloud base height retrieved by a suite of A-train satellite instruments (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) are compared to ship observations from research cruises made in 2001 and 2003–2007 into the stratus/stratocumulus deck over the southeast Pacific Ocean. It is found that CloudSat radar-only LWP is generally too high over this region and the CloudSat/CALIPSO cloud bases are too low. This results in a relationship (LWP~h9) between CloudSat LWP and CALIPSO cloud thickness (h) that is very different from the adiabatic relationship (LWP~h2) from in situ observations. Such biases can be reduced if LWPs suspected to be contaminated by precipitation are eliminated, as determined by the maximum radar reflectivity Zmax>−15 dBZ in the apparent lower half of the cloud, and if cloud bases are determined based upon the adiabatically-determined cloud thickness (h~LWP1/2). Furthermore, comparing results from a global model (CAM3.1) to ship observations reveals that, while the simulated LWP is quite reasonable, the model cloud is too thick and too low, allowing the model to have LWPs that are almost independent of h. This model can also obtain a reasonable diurnal cycle in LWP and cloud fraction at a location roughly in the centre of this region (20° S, 85° W) but has an opposite diurnal cycle to those observed aboard ship at a location closer to the coast (20° S, 75° W). The diurnal cycle at the latter location is slightly improved in the newest version of the model (CAM4). However, the simulated clouds remain too thick and too low, as cloud bases are usually at or near the surface.

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