Erratum: Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA.

This corrects the article DOI: 10.1038/sdata.2018.16.

Inter-comparison of multiple statistically downscaled climate datasets for the Pacific Northwest, USA.

Statistically downscaled climate data have been widely used to explore possible impacts of climate change in various fields of study. Although many studies have focused on characterizing differences in the downscaling methods, few studies have evaluated actual downscaled datasets being distributed publicly. Spatially focusing on the Pacific Northwest, we compare five statistically downscaled climate datasets distributed publicly in the US: ClimateNA, NASA NEX-DCP30, MACAv2-METDATA, MACAv2-LIVNEH and WorldClim. We compare the downscaled projections of climate change, and the associated observational data used as training data for downscaling. We map and quantify the variability among the datasets and characterize the spatio-temporal patterns of agreement and disagreement among the datasets. Pair-wise comparisons of datasets identify the coast and high-elevation areas as areas of disagreement for temperature. For precipitation, high-elevation areas, rainshadows and the dry, eastern portion of the study area have high dissimilarity among the datasets. By spatially aggregating the variability measures into watersheds, we develop guidance for selecting datasets within the Pacific Northwest climate change impact studies.

Modeling long-term changes in tundra carbon balance following wildfire, climate change, and potential nutrient addition.

To investigate the underlying mechanisms that control long-term recovery of tundra carbon (C) and nutrients after fire, we employed the Multiple Element Limitation (MEL) model to simulate 200-yr post-fire changes in the biogeochemistry of three sites along a burn severity gradient in response to increases in air temperature, CO2 concentration, nitrogen (N) deposition, and phosphorus (P) weathering rates. The simulations were conducted for severely burned, moderately burned, and unburned arctic tundra. Our simulations indicated that recovery of C balance after fire was mainly determined by the internal redistribution of nutrients among ecosystem components (controlled by air temperature), rather than the supply of nutrients from external sources (e.g., nitrogen deposition and fixation, phosphorus weathering). Increases in air temperature and atmospheric CO2 concentration resulted in (1) a net transfer of nutrient from soil organic matter to vegetation and (2) higher C : nutrient ratios in vegetation and soil organic matter. These changes led to gains in vegetation biomass C but net losses in soil organic C stocks. Under a warming climate, nutrients lost in wildfire were difficult to recover because the warming-induced acceleration in nutrient cycles caused further net nutrient loss from the system through leaching. In both burned and unburned tundra, the warming-caused acceleration in nutrient cycles and increases in ecosystem C stocks were eventually constrained by increases in soil C : nutrient ratios, which increased microbial retention of plant-available nutrients in the soil. Accelerated nutrient turnover, loss of C, and increasing soil temperatures will likely result in vegetation changes, which further regulate the long-term biogeochemical succession. Our analysis should help in the assessment of tundra C budgets and of the recovery of biogeochemical function following fire, which is in turn necessary for the maintenance of wildlife habitat and tundra vegetation.

Modeling carbon-nutrient interactions during the early recovery of tundra after fire.

Fire frequency has dramatically increased in the tundra of northern Alaska, USA, which has major implications for the carbon budget of the region and the functioning of these ecosystems, which support important wildlife species. We investigated the postfire succession of plant and soil carbon (C), nitrogen (N), and phosphorus (P) fluxes and stocks along a burn severity gradient in the 2007 Anaktuvuk River fire scar in northern Alaska. Modeling results indicated that the early regrowth of postfire tundra vegetation was limited primarily by its canopy photosynthetic potential, rather than nutrient availability, because of the initially low leaf area and relatively high inorganic N and P concentrations in soil. Our simulations indicated that the postfire recovery of tundra vegetation was sustained predominantly by the uptake of residual inorganic N (i.e., in the remaining ash), and the redistribution of N and P from soil organic matter to vegetation. Although residual nutrients in ash were higher in the severe burn than the moderate burn, the moderate burn recovered faster because of the higher remaining biomass and consequent photosynthetic potential. Residual nutrients in ash allowed both burn sites to recover and exceed the unburned site in both aboveground biomass and production five years after the fire. The investigation of interactions among postfire C, N, and P cycles has contributed to a mechanistic understanding of the response of tundra ecosystems to fire disturbance. Our study provided insight on how the trajectory of recovery of tundra from wildfire is regulated during early succession.

Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model.

This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45(o)N and polewards) for the period 1900-2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.