Publications Library

This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean–atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October–November–December) and late (January–February–March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Niño–Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region.

Typically, sub-seasonal to intra-annual climate forecasts are based on ensemble mean (EM) predictions. The EM prediction provides only a part of the information available from the ensemble forecast. Here we test the null hypothesis that the observations are randomly distributed about the EM predictions using a new metric that quantifies the distance between the EM predictions from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and the observations represented by CFSv2 Reanalysis. The null hypothesis cannot be rejected in this study. Hence, we argue that the higher order statistics such as ensemble standard deviation are also needed to describe the forecast. We also show that removal of systematic errors that are a function of the forecast initialization month and lead time is a necessary pre-processing step. Finally, we show that CFSv2 provides useful ensemble climate forecasts from 0 to 9 month lead time in several regions.

Climate change detection and attribution studies rely on historical simulations using specified combinations of forcings to quantify the contributions from greenhouse gases and other forcings to observed climate change. In the last CMIP5 exercise, in addition to the so-called all-forcings simulations, which are driven with a combination of anthropogenic and natural forcings, natural forcings–only and greenhouse gas–only simulations were prioritized among other possible experiments. This study addresses the question of optimally designing this set of experiments to estimate the recent greenhouse gas–induced warming, which is highly relevant to the problem of constraining estimates of the transient climate response. Based on Monte Carlo simulations and considering experimental designs with a fixed budget for the number of simulations that modeling centers can perform, the most accurate estimate of historical greenhouse gas–induced warming is obtained with a design using a combination of all-forcings, natural forcings–only, and aerosol forcing–only simulations. An investigation of optimal ensemble sizes, given the constraint on the total number of simulations, indicates that allocating larger ensemble sizes to weaker forcings, such as natural-only, is optimal

Retreat of mountain glaciers is a significant contributor to sea-level rise and a potential threat to human populations through impacts on water availability and regional hydrology. Like most of Earth’s mountain glaciers, those in western North America are experiencing rapid mass loss. Projections of future large-scale mass change are based on surface mass balance models that are open to criticism, because they ignore or greatly simplify glacier physics. Here we use a high-resolution regional glaciation model, developed by coupling physics-based ice dynamics with a surface mass balance model, to project the fate of glaciers in western Canada. We use twenty-first-century climate scenarios from an ensemble of global climate models in our simulations; the results indicate that by 2100, the volume of glacier ice in western Canada will shrink by 70 ± 10% relative to 2005. According to our simulations, few glaciers will remain in the Interior and Rockies regions, but maritime glaciers, in particular those in northwestern British Columbia, will survive in a diminished state. We project the maximum rate of ice volume loss, corresponding to peak input of deglacial meltwater to streams and rivers, to occur around 2020–2040. Potential implications include impacts on aquatic ecosystems, agriculture, forestry, alpine tourism and water quality.

The Arctic has warmed significantly more than global mean surface air temperature over recent decades, as expected from amplification mechanisms. Previous studies have attributed the observed Arctic warming to the combined effect of greenhouse gases and other anthropogenic influences. However, given the sensitivity of the Arctic to external forcing and the intense interest in the effects of aerosols on its climate, it is important to examine and quantify the effects of individual groups of anthropogenic forcing agents. Here we quantify the separate contributions to observed Arctic land temperature change from greenhouse gases, other anthropogenic forcing agents (which are dominated by aerosols) and natural forcing agents. We show that although increases in greenhouse-gas concentrations have driven the observed warming over the past century, approximately 60% of the greenhouse-gas-induced warming has been offset by the combined response to other anthropogenic forcings, which is substantially greater than the fraction of global greenhouse-gas-induced warming that has been offset by these forcings. The climate models considered on average simulate the amplitude of response to anthropogenic forcings well, increasing confidence in their projections of profound future Arctic climate change.

Climate change detection and attribution studies rely on historical simulations using specified combinations of forcings to quantify the contributions from greenhouse gases and other forcings to observed climate change. In the last CMIP5 exercise, in addition to the so-called ALL-forcings simulations which are driven with a combination of anthropogenic and natural forcings, natural forcings-only and greenhouse-gas-only simulations were prioritized among other possible experiments. This study addresses the question of optimally designing this set of experiments to estimate the recent greenhouse-gas-induced warming, which is highly relevant to the problem of constraining estimates of the transient climate response. Based on Monte Carlo simulations and considering experimental designs with a fixed budget for the number of simulations that modelling centres can perform, we find that the most accurate estimate of historical greenhouse-gas-induced warming is obtained with a design using a combination of ALL-forcings, natural forcings-only and aerosol forcing-only simulations. An investigation of optimal ensemble sizes, given the constraint on the total number of simulations, indicates that allocating larger ensemble sizes to weaker forcings, such as natural-only, is optimal.

To identify agricultural water requirements and the potential for water conservation, we have developed a GIS based model to estimate water demand based on crop type, seasonal crop development, and spatial distribution. Multiple data layers characterize topography, surface and subsurface hydrology, crop distribution, irrigation management, soils, and a range of geographic and political boundaries. The model is driven 500×500 m gridded climate data for the years 1960-2006 and future climate data sets downscaled from GCM output (12 scenarios) from 1961-2100. These climate surfaces have been used to provide calculations of Penman Monteith reference evapotranspiration and a range of agroclimatic indices for each climate grid cell, in addition to crop and terrain based irrigation water demand. Sample scenarios of changing climate, expanded agriculture, and urbanization demonstrate the usefulness of this type of modeling for regional water planning.

While researchers have identified teleconnections between El Niño-Southern Oscillation (ENSO) and extended winter precipitation extremes in North America using generalized extreme value (GEV) models, the regional form of the statistical relationship remains an open question. Past work has shown that relatively warm ENSO conditions may be needed to trigger a nonlinear response over North America. However, studies that stratify winters into La Niña, neutral, and El Niño phases have found that precipitation extremes in neutral/La Niña winters respond differently than in El Niño winters, whereas studies that stratify ENSO data into cold/warm conditions have not found evidence for a coherent nonlinear response. Data and methodological differences have made direct comparison between results difficult. In this study, evidence for a nonlinear association between ENSO and precipitation extremes is reassessed by fitting stationary and linear/nonlinear GEV regression models, with the Niño3.4 index as a covariate, to 1-, 5-, and 10-day extended winter precipitation maxima. Two types of nonlinear model – both one-sided GEV regressions where linear relationships are allowed to differ above/below a Niño3.4 index threshold – are considered. In the first, following past work, the breakpoint is fixed at zero, that is stratifying data into warm/cold conditions. In the second, to see whether there is evidence for a differential neutral/La Niña and El Niño response, the breakpoint is allowed to vary freely. Due to the non-nested nature of the set of models, the Akaike Information Criterion is used to assess the relative support for each model. Depending on accumulation time-scale, the strength of evidence favours nonlinear models at 28%–30% of stations when the Niño3.4 breakpoint is free to vary, versus just 3% with a fixed breakpoint. Optimum Niño3.4 breakpoints are positive (> +0.4°C) in the majority of the nonlinear models, confirming that ENSO/precipitation relationships differs between La Niña/neutral and El Niño winters.

Bolivia's forests contribute to the global carbon and water cycle, as well as to global biodiversity. The survival of these forests may be at risk due to climate change. To explore the associated mechanisms and uncertainties, a regionally adapted dynamic vegetation model was implemented for the Bolivian case, and forced with two contrasting climate change projections. Changes in carbon stocks and fluxes were evaluated, factoring out the individual contributions of atmospheric carbon dioxide ([CO2]), temperature, and precipitation. Impacts ranged from a strong increase to a severe loss of vegetation carbon (cv), depending on differences in climate projections, as well as the physiological response to rising [CO2]. The loss of cv simulated for an extremely dry projection was primarily driven by a reduction in gross primary productivity, and secondarily by enhanced emissions from fires and autotrophic respiration. In the wet forest, less precipitation and higher temperatures equally reduced cv, while in the dry forest, the impact of precipitation was dominating. The temperature-related reduction of cv was mainly due to a decrease in photosynthesis and only to lesser extent because of more autotrophic respiration and less stomatal conductance as a response to an increasing atmospheric evaporative demand. Under an extremely dry projection, tropical dry forests were simulated to virtually disappear, regardless of the potential fertilizing effect of rising [CO2]. This suggests a higher risk for forest loss along the drier southern fringe of the Amazon if annual precipitation will decrease substantially.