As the rate and magnitude of climate change accelerate, understanding the consequences becomes increasingly important. Species distribution models (SDMs) based on current ecological niche constraints are used to project future species distributions. These models contain assumptions that add to the uncertainty in model projections stemming from the structure of the models, the algorithms used to translate niche associations into distributional probabilities, the quality and quantity of data, and mismatches between the scales of modeling and data. We illustrate the application of SDMs using two climate models and two distributional algorithms, together with information on distributional shifts in vegetation types, to project fine-scale future distributions of 60 California landbird species. Most species are projected to decrease in distribution by 2070. Changes in total species richness vary over the state, with large losses of species in some ''hotspots'' of vulnerability. Differences in distributional shifts among species will change species co-occurrences, creating spatial variation in similarities between current and future assemblages. We use these analyses to consider how assumptions can be addressed and uncertainties reduced. SDMs can provide a useful way to incorporate future conditions into conservation and management practices and decisions, but the uncertainties of model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. Doing this will require that the sources and magnitudes of uncertainty are documented, and that conservationists and resource managers be willing to act despite the uncertainties. The alternative, of ignoring the future, is not an option.birds ͉ California ͉ ecological niche ͉ species distribution models ͉ conservation

The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II. This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations is determined, comparing the model performances with each other as well as with other regional model evaluations over North America. The metrics used herein do differentiate among the models but, as found in previous studies, it is not possible to determine a “best” model among them. The ensemble average of the six models does not perform best for all measures, as has been reported in a number of global climate model studies. The subset ensemble of the two models using spectral nudging is more often successful for domain-wide root-mean-square error (RMSE), especially for temperature. This evaluation phase of NARCCAP will inform later program elements concerning differentially weighting the models for use in producing robust regional probabilities of future climate change.

[1] Regional detection of a greenhouse warming signal relies on extensive, long-term measurements of temperature. The potentially confounding impact of land-cover and landuse change on trends in temperature records has mostly focused on the influence of urban heat islands. Here we use a regional climate model to show that a regional irrigation cooling effect (ICE) exists, opposite in sign to urban heat island effects. The magnitude of the ICE has strong seasonal variability, causing large dry-season decreases in monthly mean and maximum temperatures, but little change in rainyseason temperatures. Our model produced a negligible effect on monthly minimum temperature. In California, the modeled regional ICE is of similar magnitude, but opposite sign, to predictions for future regional warming from greenhouse gases. Given our results for California and the global importance of irrigated agriculture, past expansion of irrigated land has likely affected observations of surface temperature, potentially masking the full warming signal caused by greenhouse gas increases.

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs Climatic Change (2013) driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.

[1] Observations show that wind-driven upwelling along the California coast has increased over the past 30 years. Some have postulated that the increase in wind-driven upwelling is due largely to increased greenhouse gas forcing, but such an association has been speculative. Since global and regional simulations of future wind-driven upwelling do not exist for the California coast, we used a regional climate model (RCM) to estimate changes in wind-driven upwelling under increased CO 2 concentrations. Here we show in both equilibrium and transient climate experiments that there is an intensified upwelling season, with some changes in seasonality of upwelling. This intensification may lead to enhanced productivity along the coast of California and possibly ameliorate increases in sea surface temperature due to greenhouse gas forcing.