CGCM3 Model Research Articles

Evaluation of climate change impacts and effectiveness of adaptation options on nitrate loss, microbial respiration, and soil organic carbon in the Southeastern USA

CONTEXTClimate change presents an agricultural challenge in the Southeastern USA with implications for maintaining environmental quality. OBJECTIVEThe objective of this study was to assess climate change impacts and adaptation practices (biochar and irrigation) simulated with the Environmental Policy Integrated Climate (EPIC) model on nitrate-N (NO3-N) losses, microbial respiration (MR) and soil organic carbon (SOC) in the Southeastern USA. METHODSThe EPIC model was used to assess the impacts of climate change and adaptations on NO3-N losses in leachate and runoff from the soil profile (0–100 cm), loss of soil C via MR (0–100 cm), and impacts on SOC stocks (0–10 cm) for representative farms growing C3 and C4 crops within ten Southeastern USA states. The adaptations explored were annual biochar applications and irrigation. Historical baseline (1979–2009) and future (2041–2070) climate scenarios were used for simulations with CO2 concentrations of 360 ppm and 500 ppm, respectively. Four regional climate models (RCMs), nested within global climate models (GCMs) for their boundary conditions, simulated changes in air temperatures, precipitation, and solar radiation. RESULTS AND CONCLUSIONSClimate change increased simulated NO3-N losses in leaching and runoff by 40–80%, compared to historical baseline scenarios that was attributed to overall increased annual precipitation under three of the four RCM_GCM models. For the C4 crops, NO3-N leaching and runoff losses were 16–47% and 31–45% lower than for the C3 crops, respectively. Biochar applications reduced NO3-N leaching in the West region during 2066–2070 under the RCM3_CGCM3 model. The differences in MR between the C4 and C3 crops ranged from 3 to 75%. SOC increased under C4 crops and when biochar was applied. We concluded that inclusion of C4 crops in crop rotations and the applications of biochar under wetter climate scenarios may be a promising adaptation strategy to reduce NO3-N losses and increase SOC content in the soils of the Southeastern USA. SIGNIFICANCEThis study represents one of the first attempts to assess the effectiveness of climate change adaptations such as the agricultural use of biochar and irrigation. The findings from this study strongly contribute to our understanding of potential climate change impacts on a region's agriculture and resulting environmental footprint. This information may be used by the scientific community along with decision and policy makers working on conceptual and practical technologies to mitigate the impacts of climate change on agriculture and the environment.

Evaluation of CMIP5 models and projected changes in temperatures over South Asia under global warming of 1.5 oC, 2 oC, and 3 oC

This study was designed to evaluate the spatiotemporal performance of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models in the historical simulation and future projections of minimum (Tmin), maximum (Tmax), and mean temperature (Tmean) over South Asia (SA) during global warming of 1.5 oC, 2 oC, and 3 oC targets under RCP4.5 and RCP8.5 scenarios. It is worth mentioning that the present study is the first of its kind to use such a large number of CMIP5 models to project future changes in Tmin, Tmax, and Tmean over SA using three different warming thresholds. The results show that CSIRO–MK3–6–0, MIROC–ESM–CHEM, CNRM–CM5, CCSM4, and MRI–CGCM3 models relatively performed better with a consistent and accurate spatiotemporal simulation of Tmin, Tmax, and Tmean over SA. In terms of projected changes, Tmin, Tmax, and Tmean show a dominating and consistent warming pattern over SA with stronger intensity in higher latitude than mid–low latitudes under 1.5 oC, 2 oC, and 3 oC warming thresholds. The northwestern (eastern) regions of SA will witness greater (least) warming in Tmin, Tmax, and Tmean under all warming thresholds in RCP4.5 and RCP8.5 scenarios. Furthermore, the central and southern parts of SA will experience a moderate increase in Tmin, Tmax, and Tmean under all warming targets. The uneven and intensified patterns of Tmin, Tmax, and Tmean may result in temperature extremes, which would pose potential risks to the local population. Therefore, more attention should be paid on the regional and local perspectives to estimate the adverse impacts of these extremes under different global warming targets. We further suggest to project future changes in climate extremes over SA under different warming levels, which will be helpful in climate change adaptation and mitigation over the study region.

ANN-based statistical downscaling of climatic parameters using decision tree predictor screening method

In this paper, artificial neural network (ANN) was used for statistically downscale the outputs of general circulation models (GCMs) to assess future changes of precipitation and mean temperature in Tabriz synoptic station at north-west of Iran. Since one of the significant subjects in statistical downscaling of GCMs is to select the most dominant large scale climate variables (predictors) among huge number of potential predictors, the predictors screening methods including decision tree, mutual information (MI) and correlation coefficient (CC) were used to statistically downscale mean monthly precipitation and temperature. Three GCMs were used, including Can-ESM2 and BNU-ESM from IPCC AR5 models and CGCM3 from IPCC AR4 models. The results of downscaling in the base period (1951–2000) indicated that among feature extraction methods decision tree had superiority to MI and CC techniques. Therefore, the future projection of precipitation and mean temperature during 2020–2060 was implemented using ANN-based simulation according to the most efficient downscaling model (i.e., decision tree-based calibration). Different results according to different GCMs and scenarios were obtained for precipitation projection. In this way, the Can-ESM2 model under RCP8.5 showed 29.78% decrease in annual precipitation and CGCM3 model under B1 indicated 1.06% increase of annual precipitation. Temperature projection outcomes denoted that annual mean temperature will increase over the region and the most increase in mean temperature was determined by BNU-ESM model under RCP8.5.

Downscaling of Future Temperature and Precipitation Extremes in Addis Ababa under Climate Change

One of the recent advances in climate science research is the development of global general circulation models (GCMs) to simulate changes in climatic elements of the present and future, which helps us to determine consequences earlier and prepare for necessary adaptation measures. However, it is difficult to apply the raw data of GCMs at a local scale, such as the urban scale, without downscaling due to coarse resolution. This study, therefore, statistically downscaled daily maximum temperature, minimum temperature, and precipitation in 30-year intervals from the second generation of the Earth System Model (CanESM2) and Coupled Global Climate Model (CGCM3) under two Representative Concentration Pathways (RCP) Scenarios (RCP4.5 and RCP8.5) and two Special Report Emission Scenarios (SRES), A1B and A2, to examine future changes and their extremes. Two representative meteorological stations (Entoto at high elevation and Addis Ababa at downtown and medium elevation) were selected for model calibration and validation in the Statistical Downscaling Model (SDSM). Twelve core temperature and precipitation indices were selected to assess temperature changes and precipitation extremes. For the largest changes the results showed that the maximum temperature increases were in the range of 0.9 °C (RCP4.5) in 2020 to 2.1 °C (CGCM3A2) in 2080 at Addis Ababa Observatory. The minimum temperature is projected to increase by 0.3 °C (RCP4.5) in 2020 and 1.0 °C in 2080 (CGCM3A1B). While the changes in maximum temperature are lower at Entoto station compared to Addis Ababa Observatory, the highest minimum temperature change is projected at Addis Ababa Observatory, which ranges from 0.25 °C in the 2020s to 1.04 °C in 2080 according to the CGCM3 model. Except for the coldest nights (TNn), the mean temperature and other temperature indices will continue to increase to the end of this century. The highest precipitation change is projected by CGCM3A2 and CanESM2 RCP8.5 at an increase of about 11.8% and 16.62% by 2080. The highest total precipitation increase is 29% (RCP4.5) in winter and 20.9% (RCP8.5) in summer by 2080. There is high interseasonal variability in changes of extreme events. The topographic role will diminish in influence on the air temperature distribution due to the high rate of urbanization. The rise in temperature will exacerbate the urban heat highland effects in warm seasons and an increase in precipitation is expected along with a possible risk of flooding due to a low level of infrastructure development and a high rate of urbanization.

Comparison of CGCM3, CSIRO MK3 and HADCM3 Models in Estimating the Effects of Climate Change on Temperature and Precipitation in Taleghan Basin

Climate change is a significant change in weather or in its variability in a long period. This change could be in the average of temperature, rainfall, humidity, weather patterns, wind, and sunlight and so on. Atmosphere-Ocean General Circulation Model (AOGCMs) have been developed to simulate current climate of the planet and are able to predict future climate change of the Earth. In this paper the performance of CGCM3, CSIRO Mk3 and HadCM3 models in estimating the effects of climate change on temperature and precipitation of the Taleghan basin were studied. The results show that despite relatively acceptable performance of all three models in temperature modeling, it seems that the outputs of HadCM3 model for this basin are more desirable when compared with CGCM3 and CSIRO MK3 models and it is can be said that HadCM3 seems to be more reliable for this basin.

Climate Modeling of Jhelum River Basin-A Comparative Study

Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at regional scale, the GCM outputs cannot be used directly due to the mismatch in the spatial resolution between the GCMs and hydrological models. In order to use the output of a GCM for conducting hydrological impact studies, downscaling is used. Among the variables usually downscaled, precipitation and temperature downscaling is quite challenging and is more prone to uncertainty issues than other climatological variables. The downscaling methods used for this study belong to the following downscaling categories; (1) Multiple linear regression; and (2) Artificial neural network. In this study the future projections of climate were made under A1B scenario up to 21st century using CGCM3 model. At the end of the 21st century the mean annual temperature of the Jhelum river basin is predicted to increase by 2.36°C and 1.43°C under A1B scenario using MLR and ANN technique respectively whereas the total annual precipitation is predicted to decrease substantially by 36.53% and 30.88% under A1B scenario by MLR and ANN technique, respectively.

MLR Based Statistical Downscaling of Temperature and Precipitation in Lidder Basin Region of India

In the present study, the multiple linear regression technique was employed to relate the monthly predictors obtained from the Global Climate Model namely CGCM3 (Canadian Centre for Climate Modelling and Analysis) with the monthly Predictands such as the locally observed precipitation and temperature at Pahalgam meteorological station which is located in the Lidder River Basin. Appropriate predictand-predictor relationships were found out for the region by carrying out sensitivity analysis .Regression equations were developed and subsequently future monthly and annual projections for precipitation, maximum temperature and minimum temperature for the entire 21st century were made. It was observed that at the end of the 21st century the CGCM3 model predicted an increase of 18.07% annual precipitation, whereas the average maximum and minimum annual temperatures were predicted to increase by 0.62°C and 0.76°C, respectively.

GCMs Derived Projection of Precipitation and Analysis of Spatio-Temporal Variation over N-W Himalayan Region

The ensembles of two Global Climate Models (GCMs) namely, third generation Canadian Coupled Global Climate Model (CGCM3) and Hadley Center Coupled Model, version 3 (HadCM3) are used to project future precipitation in a part of North-Western (N-W) Himalayan region, India. Statistical downscaling method is used to downscale and generate future scenarios of precipitation at station scale from large scale climate variables obtained from GCMs. The observed historical precipitation data has been collected for three metrological stations, namely, Rampur, Sunni and Kasol falling in the basin for further analysis. The future trends and patterns in precipitation under scenarios A2 and A1B for CGCM3 model, and A2 and B2 for HadCM3 model are analyzed for these stations under three different time periods: 2020’s, 2050’s and 2080’s. An overall rise in mean annual precipitation under scenarios A2 and A1B for CGCM3 model have been noticed for future periods: 2020’s, 2050’s and 2080’s. Decrease, in precipitation has been found under A2 and B2 scenarios of HadCM3 model for 2050’s and slight increase for 2080’s periods. Based on the analysis of results, CGCM3 model has been found better for simulation of precipitation in comparison to HadCM3 model.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.1-14

Assessment of future changes in the maximum temperature at selected stations in Iran based on HADCM3 and CGCM3 models

Identification and assessment of climate change in the next decades with the aim of appropriate environmental planning in order to adapt and mitigate its effects are quite necessary. In this study, maximum temperature changes of Iran were comparatively examined in two future periods (2041-2070 and 2071-2099) and based on the two general circulation model outputs (CGCM3 and HADCM3) and under existing emission scenarios (A2, A1B, B1 and B2). For this purpose, after examining the ability of statistical downscaling method of SDSM in simulation of the observational period (1981-2010), the daily maximum temperature of future decades was downscaled by considering the uncertainty in seven synoptic stations as representatives of climate in Iran. In uncertainty analysis related to model-scenarios, it was found that CGCM3 model under scenario B1 had the best performance about the simulation of future maximum temperature among all of the examined scenario-models. The findings also showed that the maximum temperature at study stations will be increased between 1°C and 2°C in the middle and the end of 21st century. Also this maximum temperature changes is more severe in the HADCM3 model than the CGCM3 model.

Future changes in maximum temperature using the statistical downscaling model (SDSM) at selected stations of Iran

In the 21st century, climate change in the dry lands is considerate to be one of the greatest environmental threats to the world. This study presents the projection of future changes in maximum temperatures under A2, A1B and B1 SRES of the CGCM3 outputs using the statistical downscaling model (SDSM) in the 7 selected station over Iran during the two future periods (2041–70 and 2071–99). For this purpose, after examining the ability of SDSM in simulation of the basic period climate (1981–2010), the daily maximum temperature for future decades was downscaled by considering the uncertainty in 7 synoptic stations as climatic representatives of Iran. In uncertainties analysis related to model-scenarios, it was found that CGCM3 model under scenario B1 had the best performance in the simulation of future maximum temperatures among all of the scenarios. The findings showed that average maximum temperature at study stations over Iran will be increased between 0.3 and 3.5 °C. Also this maximum temperature changes will be more severe increased based on scenario A2 compared to other scenarios of the CGCM3 model.

Assessment of Climate Change for Precipitation and Temperature Using Statistical Downscaling Methods in Upper Godavari River Basin, India

In the present study SDSM downscaling model was used as a tool for downscaling weather data statistically in upper Godavari river basin. Two Global Climate Models (GCMs), CGCM3 and HadCM3, have been used to project future maximum temperature (Tmax), minimum temperature (Tmin) and precipitation. The predictor variables are extracted from: 1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1961-2003, 2) the simulations from the third-generation Hadlycentre Coupled Climate Model (HadCM3) and Coupled Global Climate Model (CGCM3) variability and changes in Tmax, Tmin and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model have been presented for future periods: 2020s, 2050s and 2080s. The scatter-plots and cross-correlations are used for verifying the reliability of the simulation. Maximum temperature increases in future for almost all the scenarios for both GCMs. Also downscaled future precipitation shows increasing trends for all scenarios.

Assessment of impact of climate change on water resources in a hilly river basin

The sensitivity of Sutlej river sub-basin (middle catchment) which is located in N-W Himalaya is investigated for its hydrologic response to potential changes in climate variability. The predictors of two global climate models (GCMs) that are found to perform well over Indian sub-continents are downscaled, and future time series of temperature (maximum and minimum) and precipitation is generated using statistical downscaling model (SDSM) under A1B, A2, and B2 emission scenarios. An overall increase in mean annual temperature and precipitation is predicted under both the models for future periods. The predicted increase in temperature is relatively higher for HadCM3 model compares to CGCM3 model whereas it is opposite for precipitation. The model also predicts considerable shift in monthly pattern of temperature and precipitation. Further, soil and water assessment tool (SWAT) is employed to appraise future changes in stream flow and water balance of the sub-basin under projected climate scenarios. The simulation results show that in future, increase in mean annual stream flow are likely to range from 1.3 to 7.8 % for CGCM3 model and 0.3 to 3.4 % for HadCM3 model, respectively. However, decrease in mean monthly stream flow is predicted under scenarios of CGCM3 model (from January to May and from November to December) and HadCM3 model (from January to April and from October to December), respectively.

Statistical downscaling and projection of future temperature and precipitation change in middle catchment of Sutlej River Basin, India

Ensembles of two Global Climate Models (GCMs), CGCM3 and HadCM3, are used to project future maximum temperature (T Max), minimum temperature (T Min) and precipitation in a part of Sutlej River Basin, northwestern Himalayan region, India. Large scale atmospheric variables of CGCM3 and HadCM3 under different emission scenarios and the National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis datasets are downscaled using Statistical Downscaling Model (SDSM). Variability and changes in T Max, T Min and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model are presented for future periods: 2020s, 2050s and 2080s. The study reveals rise in annual average T Max, T Min and precipitation under scenarios A1B and A2 for CGCM3 model as well as under A2 and B2 scenarios for HadCM3 model in 2020s, 2050s and 2080s. Increase in mean monthly T Min is also observed for all months of the year under all scenarios of both the models. This is followed by decrease in T Max during June, July August and September. However, the model projects rise in precipitation in months of July, August and September under A1B and A2 scenarios of CGCM3 model and A2 and B2 of HadCM3 model for future periods.

Statistical analysis of long term spatial and temporal trends of temperature parameters over Sutlej river basin, India

The annual and seasonal trend analysis of different surface temperature parameters (average, maximum, minimum and diurnal temperature range) has been done for historical (1971–2005) and future periods (2011–2099) in the middle catchment of Sutlej river basin, India. The future time series of temperature data has been generated through statistical downscaling from large scale predictors of CGCM3 and HadCM3 models under A2 scenario. Modified Mann–Kendall test and Cumulative Sum (CUSUM) chart have been used for detecting trend and sequential shift in time series of temperature parameters. The results of annual trend analysis for period of 1971–2005 show increasing as well as decreasing trends in average (T Mean), maximum (T Max), minimum (T Min) temperature and increasing trends in Diurnal Temperature Range (DTR) at different stations. But the annual trend analysis of downscaled data has revealed statistically significant (95% confidence level) rising trends in T Mean, T Max, T Min and falling trend in DTR for the period 2011–2099. The decreasing trend in DTR is due to higher rate of increase in T Min compared to T Max.

Climate change scenarios of surface solar radiation in data sparse regions: a case study in Malaprabha River Basin, India

A variety of methods are available to estimate future solar radiation (SR) scenarios at spatial scales that are appropriate for local climate change impact assessment. However, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. Three methodologies to guide the estimation of SR are discussed in this study, namely: Case 1: SR is measured, Case 2: SR is measured but sparse and Case 3: SR is not measured. In Case 1, future SR scenarios are derived using several downscaling methodologies that transfer the simulated large-scale information of global climate models to a local scale ( measurements). In Case 2, the SR was first estimated at the local scale for a longer time period using sparse measured records, and then future scenarios were derived using several downscaling methodologies. In Case 3: the SR was first estimated at a regional scale for a longer time period using complete or sparse measured records of SR from which SR at the local scale was estimated. Finally, the future scenarios were derived using several downscaling methodologies. The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies. Hence, this was further elaborated by applying the Case 3 methodology to a semi-arid Malaprabha reservoir catchment in southern India. A support vector machine was used in downscaling SR. Future monthly scenarios of SR were estimated from simulations of third-generation Canadian General Circulation Model (CGCM3) for various SRES emission scenarios (A1B, A2, B1, and COMMIT). Results indicated a projected decrease of 0.4 to 12.2 W m(-2) yr(-1) in SR during the period 2001-2100 across the 4 scenarios. SR was calculated using the modified Hargreaves method. The decreasing trends for the future were in agreement with the simulations of SR from the CGCM3 model directly obtained for the 4 scenarios.

Attribution of temperature and precipitation changes to greenhouse gases in northwest Iran

Increase of the greenhouse gases has a considerable effect on climatic variables changes such as temperature and precipitation. Three-dimensional models of coupled ocean–atmosphere general circulation, AOGCMs, under all emissions scenarios, are able to simulate past and future climate periods. The present study investigates the possible attribution of climate change to the increase of the greenhouse gases in northwest Iran. Interannual variability range for the long-term (1000 years) of temperature and precipitation, resulting from control run (greenhouse gas constant) CGCM3 model used for the study area in northwest Iran. Then, the range of natural climate variables was determined from two-dimensional graphs of temperature and precipitation based on two-variant normal distribution. Ultimately, the detected trends of climate variables can be compared with the natural climate variability range of the region. The results showed that in different parts of the study area, the range of natural climate variables for temperature and precipitation changes (95% probability) in the west of the study area are less than 1.8 C° and 40%, respectively. These values are less than 4 C° and 40% respectively, in the east of the study area. However, the climate variables in the most regions of the west are almost outside the range of natural climate variables in the recent years (1998–2008). The result indicates the effect of climate changes on the climatic variables for the case study in recent years.

Persistent climate corridors: The identification of climate refugia for the selection of candidate areas for conservation.

Climate driven changes are catalyzing the global re-distribution of species and ecosystems, and is threatening their persistence. These changes undermine the current conservation paradigm that has a static approach to a dynamic system. Conservation planning agencies, such as the Nature Conservancy of Canada (NCC) recognize this quandary and have started to incorporate the potential (though uncertain) impacts of climate change into their planning framework. As a component of NCC’s Central Interior Ecoregional Assessment, we identified bioclimatic envelopes for 206 conservation targets (103 BC biogeoclimatic (BGC) variants, 30 terrestrial ecological units (TEUs), 73 BC Conservation Data Centre plant species) using ClimateBC and ArcMap GIS software. Using ClimateBC interpolations of current and expected future (as projected by the CGCM3 model and the A2 scenario) climatic conditions, the locations meeting the 5th through 95th percentile requirements of a target’s bioclimatic envelope were identified for four timeslices (baseline (1960s-1990s), 2020s, 2050s, 2080s) and subsequently overlaid using ArcMap. The points of coincidence between these areas were identified as a target’s projected suitable climate space (SCS); locations or areas of a target’s current distribution which coincided with its SCS were identified as the target’s persistent climate corridor (PCC). Our results projected PCCs for only 10% (10/103) of the BGC variants, 20% (6/30) of the TEUs and 10% (7/73) of plant species. When comparing the projected results with those derived for three different GCM and cenario combinations, it is clear that the existence and locations of PCCs are subject to great uncertainty. Nevertheless, we argue that the identification of climate refugia should be an important consideration in the site selection and prioritization of candidate areas for conservation.

Projected loss of a salamander diversity hotspot as a consequence of projected global climate change.

BackgroundSignificant shifts in climate are considered a threat to plants and animals with significant physiological limitations and limited dispersal abilities. The southern Appalachian Mountains are a global hotspot for plethodontid salamander diversity. Plethodontids are lungless ectotherms, so their ecology is strongly governed by temperature and precipitation. Many plethodontid species in southern Appalachia exist in high elevation habitats that may be at or near their thermal maxima, and may also have limited dispersal abilities across warmer valley bottoms.Methodology/Principal FindingsWe used a maximum-entropy approach (program Maxent) to model the suitable climatic habitat of 41 plethodontid salamander species inhabiting the Appalachian Highlands region (33 individual species and eight species included within two species complexes). We evaluated the relative change in suitable climatic habitat for these species in the Appalachian Highlands from the current climate to the years 2020, 2050, and 2080, using both the HADCM3 and the CGCM3 models, each under low and high CO2 scenarios, and using two-model thresholds levels (relative suitability thresholds for determining suitable/unsuitable range), for a total of 8 scenarios per species.Conclusion/SignificanceWhile models differed slightly, every scenario projected significant declines in suitable habitat within the Appalachian Highlands as early as 2020. Species with more southern ranges and with smaller ranges had larger projected habitat loss. Despite significant differences in projected precipitation changes to the region, projections did not differ significantly between global circulation models. CO2 emissions scenario and model threshold had small effects on projected habitat loss by 2020, but did not affect longer-term projections. Results of this study indicate that choice of model threshold and CO2 emissions scenario affect short-term projected shifts in climatic distributions of species; however, these factors and choice of global circulation model have relatively small affects on what is significant projected loss of habitat for many salamander species that currently occupy the Appalachian Highlands.

PAST AND FUTURE CHANGES IN CANADIAN BOREAL WILDFIRE ACTIVITY

Climate change in Canadian boreal forests is usually associated with increased drought severity and fire activity. However, future fire activity could well be within the range of values experienced during the preindustrial period. In this study, we contrast 21st century forecasts of fire occurrence (FireOcc, number of large forest fires per year) in the southern part of the Boreal Shield, Canada, with the historical range of the past 240 years statistically reconstructed from tree-ring width data. First, a historical relationship between drought indices and FireOcc is developed over the calibration period 1959-1998. Next, together with seven tree-ring based drought reconstructions covering the last 240 years and simulations from the CGCM3 and ECHAM4 global climate models, the calibration model is used to estimate past (prior to 1959) and future (post 1999) FireOcc. Last, time-dependent changes in mean FireOcc and in the occurrence rate of extreme fire years are evaluated with the aid of advanced methods of statistical time series analysis. Results suggest that the increase in precipitation projected toward the end of the 21st century will be insufficient to compensate for increasing temperatures and will be insufficient to maintain potential evapotranspiration at current levels. Limited moisture availability would cause FireOcc to increase as well. But will future FireOcc exceed its historical range? The results obtained from our approach suggest high probabilities of seeing future FireOcc reach the upper limit of the historical range. Predictions, which are essentially weighed on northwestern Ontario and eastern boreal Manitoba, indicate that, by 2061-2100, typical FireOcc could increase by more than 34% when compared with the past two centuries. Increases in fire activity as projected by this study could negatively affect the implementation in the next century of forest management inspired by historical or natural disturbance dynamics. This approach is indeed feasible only if current and future fire activities are sufficiently low compared with the preindustrial fire activity, so a substitution of fire by forest management could occur without elevating the overall frequency of disturbance. Conceivable management options will likely have to be directed toward minimizing the adverse impacts of the increasing fire activity.