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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.

Two recently published articles serve to answer two questions about the response of the Earth’s climate to carbon emissions. The first paper, by Goodwin et al. (2014) in Nature Geoscience, investigates the question of why transient surface warming on the timescale of decades to centuries, due to cumulative carbon emissions, is nearly-linear. They find that this is the result of the competing effects of the ocean absorbing both heat and carbon. While the former initially reduces climate sensitivity by drawing down heat, it then increases climate sensitivity as this heat absorption reduces. This is offset by the latter, as the ocean removes carbon dioxide from the air. The authors also find, in line with previous research, that increasing emissions lead to increased surface warming and that this warming will last many centuries.

The second article, by Ricke and Caldeira (2014) in Environmental Research Letters, uses model output to analyze the response of the Earth’s climate to pulses of carbon dioxide in order to answer the question of how long it takes for maximum warming to occur due to a given emission. They find that the median time between such an emission and the maximum warming due to that emission is 10.1 years. Their results lead the authors to state that, “[o]ur results indicate that benefit from avoided CO2 emissions will be manifested within the lifetimes of people who acted to avoid [those emissions].”

Logistical constraints can limit the number of global climate model (GCM) simulations considered in a climate change impact assessment. When dealing with annual or seasonal variables, one can visualize and manually select GCM scenarios to cover as much of the ensemble’s range of changes as possible. Most environmental systems are sensitive to climate conditions (e.g., extremes) that cannot be described by a small number of variables. Instead, algorithms like k-means clustering have been used to select representative ensemble members. Clustering algorithms are, however, biased toward high-density regions of climate variable space and tend to select scenarios that describe the central tendency rather than the full spread of an ensemble. Also, scenarios selected via clustering may not be ordered: that is, scenarios in the five-cluster solution may not appear in the six-cluster solution, which makes recommending a consistent set of scenarios to researchers with different needs difficult. Alternatively, an automated procedure based on a cluster initialization algorithm is proposed and applied to changes in 27 climate extremes indices between 1986–2005 and 2081–2100 from a large ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations. Selections by the method are ordered and are designed to span the overall range of the ensemble. The number of scenarios required to account for changes spanned by at least 90% of the CMIP5 ensemble members is reported for 21 regions of the globe and compared with k-means clustering. On average, the proposed method requires 40% fewer scenarios to meet this threshold than k-means clustering does.

Rainfall extreme value estimates in Canada have historically been based on fitting the Gumbel distribution to annual maxima at individual sites by the method of moments (MOM). Studies have, however, shown that regional frequency analyses (RFA) may perform better than at-site methods. Also, the Fréchet rather than Gumbel form of the generalized extreme value (GEV) distribution may better describe the distribution of annual extremes. In this study, at-site Gumbel MOM and GEV extreme value analyses based on L-moment, maximum likelihood (MLE), and generalized maximum likelihood (GML) estimators are compared against RFA with L-moment methods at stations in southern British Columbia, Canada via cross-validation and Monte Carlo simulations. While GEV shape parameter estimates are predominately negative, qualitatively showing weak evidence for the Fréchet form of the GEV distribution, field significant differences from the Gumbel distribution in the region are not found. Regional frequency analysis leads to substantial reductions in error relative to at-site methods, especially for the GEV distribution and small samples. While Gumbel estimators exhibit lower variance than GEV estimators, they are also more biased, underestimating 100 year return levels. Of the at-site GEV estimators, GML tended to perform better than the L-moment estimator, in some cases nearing performance of RFA. Maximum likelihood performed worst, especially for small samples sizes.

In this study the impact of climate change on runoff extremes is investigated over the Pacific Northwest (PNW). This paper aims to address the question of how the runoff extremes change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes and assess the seasonal variations of runoff extremes. Hydrologic modeling is performed by the variable infiltration capacity (VIC) model at a 1/8° resolution and the model is driven by climate scenarios provided by the North American Regional Climate Change Assessment Program (NARCCAP) including nine regional climate model (RCM) simulations. Analysis is performed for both the historical (1971–2000) and future (2041–2070) time periods. Downscaling of the climate variables including precipitation, maximum and minimum temperature and wind speed is done using the quantile-mapping (QM) approach. A spatial hierarchical Bayesian model is then developed to analyse the annual maximum runoff in different seasons for both historical and future time periods. The estimated spatial changes in extreme runoffs over the future period vary depending on the RCM driving the hydrologic model. The hierarchical Bayesian model characterizes the spatial variations in the marginal distributions of the General Extreme Value (GEV) parameters and the corresponding 100-year return level runoffs. Results show an increase in the 100-year return level runoffs for most regions in particular over the high elevation areas during winter. The Canadian portions of the study region reflect higher increases during spring. However, reduction of extreme events in several regions is projected during summer.

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.

Fully coupled global climate model experiments are performed using the Community Climate System Model version 4.0 (CCSM4) for preindustrial, present, and future climate to study the effects of realistic land surface initializations on subseasonal to seasonal climate forecasts. Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and/or model parameterizations. Findings suggest that realistic land surface initialization is important for climate predictability at subseasonal to seasonal time scales. We found the highest predictability for soil moisture, followed by evapotranspiration, temperature, and precipitation. The predictability is highest for the 16 to 30 days forecast period, and it progressively decreases for the second and third month forecasts. We found significant changes in the spatial distributions of temperature predictability in the present and future climate compared to the preindustrial climate, although the spatial average changes for North America were rather small (

Regional warming due to anthropogenic influence on the climate is expected to increase the frequency of very warm years and seasons. The growing research area of extreme event attribution has provided pertinent scientific evidence for a number of such warm events for which the forced climate response rises above internal climatic variability. Although the demand for attribution assessments is higher shortly after an event occurs, most scientific studies become available several months later. A formal attribution methodology is employed here to pre-compute the changing odds of very warm years and seasons in regions across the world. Events are defined based on the exceedence of temperature thresholds and their changing odds are measured over a range of pre-specified thresholds, which means assessments can be made as soon as a new event happens. Optimal fingerprinting provides observationally constrained estimates of the global temperature response to external forcings from which regional information is extracted. This information is combined with estimates of internal variability to construct temperature distributions with and without the effect of anthropogenic influence. The likelihood of an event is computed for each distribution and the change in the odds estimated. Analyses are conducted with seven climate models to explore the model dependency of the results. Apart from colder regions and seasons, characterised by greater internal climate variability, the odds of warm events are found to have significantly increased and temperatures above the threshold of 1-in-10 year events during 1961–1990 have become at least twice as likely to occur.

The discrepancy between recent observed and simulated trends in global mean surface temperature has provoked a debate about possible causes and implications for future climate change projections. However, little has been said in this discussion about observed and simulated trends in global temperature extremes. Here we assess trend patterns in temperature extremes and evaluate the consistency between observed and simulated temperature extremes over the past four decades (1971–2010) in comparison to the recent 15 years (1996–2010). We consider the coldest night and warmest day in a year in the observational dataset HadEX2 and in the current generation of global climate models (CMIP5). In general, the observed trends fall within the simulated range of trends, with better consistency for the longer period. Spatial trend patterns differ for the warm and cold extremes, with the warm extremes showing continuous positive trends across the globe and the cold extremes exhibiting a coherent cooling pattern across the Northern Hemisphere mid-latitudes that has emerged in the recent 15 years and is not reproduced by the models. This regional inconsistency between models and observations might be a key to understanding the recent hiatus in global mean temperature warming.