Stefan Rimkus

The role of local‐scale heterogeneities in terrestrial ecosystem modeling

The coarse-grained spatial representation of many terrestrial ecosystem models hampers the importance of local-scale heterogeneities. To address this issue, we combine a range of observations (forest inventories, eddy flux tower data, and remote sensing products) and modeling approaches with contrasting degrees of abstraction. The following models are selected: (i) Lund-Potsdam-Jena (LPJ), a well-established, area-based, dynamic global vegetation model (DGVM); (ii) LPJ-General Ecosystem Simulator, a hybrid, individual-based approach that additionally considers plant population dynamics in greater detail; and (iii) distributed in space-LPJ, a spatially explicit version of LPJ, operating at a fine spatial resolution (100 m × 100 m), which uses an enhanced hydrological representation accounting for lateral connectivity of surface and subsurface water fluxes. By comparing model simulations with a multivariate data set available at the catchment scale, we argue that (i) local environmental and topographic attributes that are often ignored or crudely represented in DGVM applications exert a strong control on terrestrial ecosystem response; (ii) the assumption of steady state vegetation and soil carbon pools at the beginning of simulation studies (e.g., under “current conditions”), as embedded in many DGVM applications, is in contradiction with the current state of many forests that are often out of equilibrium; and (iii) model evaluation against vegetation carbon fluxes does not imply an accurate simulation of vegetation carbon stocks. Having gained insights about the magnitude of aggregation-induced biases due to smoothing of spatial variability at the catchment scale, we discuss the implications of our findings with respect to the global-scale modeling studies of carbon cycle and we illustrate alternative ways forward.

On the role of subsurface heat conduction in glacier energy-balance modelling

Abstract We discuss the inclusion of the subsurface heat-conduction flux into the calculation of the energy balance and ablation at the glacier–atmosphere interface. Data from automatic weather stations are used to force an energy-balance model at several locations on alpine glaciers and at one site in the dry Andes of central Chile. The heat-conduction flux is computed using a two-layer scheme, assuming that 36% of the net shortwave radiation is absorbed by the surface layer and that the rest penetrates into the snowpack. We compare simulations conducted with and without subsurface heat flux. Results show that assuming a surface temperature of zero degrees leads to a larger overestimation of melt at the sites in the accumulation area (10.4–13.3%) than in the ablation area (0.5–2.8%), due to lower air temperatures and the presence of snow. The difference between simulations with and without heat conduction is also high at the beginning and end of the ablation season (up to 29% for the first 15 days of the season), when air temperatures are lower and snow covers the glacier surface, while they are of little importance during periods of sustained melt at all the locations investigated.

Does internal climate variability overwhelm climate change signals in streamflow? The upper Po and Rhone basin case studies.

Projections of climate change effects in streamflow are increasingly required to plan water management strategies. These projections are however largely uncertain due to the spread among climate model realizations, internal climate variability, and difficulties in transferring climate model results at the spatial and temporal scales required by catchment hydrology. A combination of a stochastic downscaling methodology and distributed hydrological modeling was used in the ACQWA project to provide projections of future streamflow (up to year 2050) for the upper Po and Rhone basins, respectively located in northern Italy and south-western Switzerland. Results suggest that internal (stochastic) climate variability is a fundamental source of uncertainty, typically comparable or larger than the projected climate change signal. Therefore, climate change effects in streamflow mean, frequency, and seasonality can be masked by natural climatic fluctuations in large parts of the analyzed regions. An exception to the overwhelming role of stochastic variability is represented by high elevation catchments fed by glaciers where streamflow is expected to be considerably reduced due to glacier retreat, with consequences appreciable in the main downstream rivers in August and September. Simulations also identify regions (west upper Rhone and Toce, Ticino river basins) where a strong precipitation increase in the February to April period projects streamflow beyond the range of natural climate variability during the melting season. This study emphasizes the importance of including internal climate variability in climate change analyses, especially when compared to the limited uncertainty that would be accounted for by few deterministic projections. The presented results could be useful in guiding more specific impact studies, although design or management decisions should be better based on reliability and vulnerability criteria as suggested by recent literature.

Uncertainty partition challenges the predictability of vital details of climate change

Decision makers and consultants are particularly interested in “detailed” information on future climate to prepare adaptation strategies and adjust design criteria. Projections of future climate at local spatial scales and fine temporal resolutions are subject to the same uncertainties as those at the global scale but the partition among uncertainty sources (emission scenarios, climate models, and internal climate variability) remains largely unquantified. At the local scale the uncertainty of the mean and extremes of precipitation is shown to be irreducible for mid and end-of-century projections because it is almost entirely due to internal climate variability (stochasticity). Conversely, projected changes in mean air temperature and other meteorological variables can be largely constrained, even at local scales, if more accurate emission scenarios can be developed. The results were obtained by applying a comprehensive stochastic downscaling technique to climate model outputs for three exemplary locations. In contrast with earlier studies, the three sources of uncertainty are considered as dependent and, therefore, non-additive. The evidence of the predominant role of internal climate variability leaves little room for uncertainty reduction in precipitation projections; however, the inference is not necessarily negative, since the uncertainty of historic observations is almost as large as that for future projections with direct implications for climate change adaptation measures.